User Acceptance Research Articles

Exploration of the Role of Virtual Reality and Augmented Reality in Revolutionizing Art Education

Virtual reality (VR) and augmented reality (AR) technologies have become increasingly important instruments in the field of art education as information technology develops quickly, transforming the conventional art education approach. The present situation, benefits, difficulties, and potential development tendencies of VR and AR technologies in art education will be investigated in this study. By means of literature analysis and case studies, this paper presents the fundamental ideas of VR and AR technologies together with their several uses in art education, namely virtual museums, interactive art production, art history instruction, and distant art cooperation. The research examines how these technologies might improve students’ immersion, raise their learning motivation, and encourage innovative ideas and multidisciplinary cooperation. Practical application concerns including technology costs, content production obstacles, user acceptance, privacy, and ethical questions also come under discussion. At last, the article offers ideas and suggestions to help VR and AR technologies be effectively integrated into art education through teacher training, curriculum design, technology infrastructure development, and multidisciplinary cooperation. This study offers useful advice for teachers of art as well as important references for legislators and technology developers working together to further the creative growth of art education.

Prospects and Challenges of Affective AI Technology in Supporting Remote Learning

This study analyzes the potential and challenges of introducing affective AI technologies to promote remote education. The growing popularity of remote learning highlights the significant upside of affective AI in real-time emotional monitoring and feedback, especially for improving learning motivation, optimizing personalized educational paths, and supporting students' mental health. This study explores the particular applications of affective AI technologies and the ethical and privacy concerns they present. The research indicates that affective AI technology, based on the principles of affective computing, may strengthen students' emotional management abilities, improve the overall educational experience by identifying their emotional states in real time, and create more possibilities for teacher-student interactions. Affective AI enhances student motivation and optimizes personalized learning paths in remote education, especially for fostering positive emotional regulation and mental health support outcomes. However, affective AI encounters challenge related to technical prejudice, privacy protection, ethical fairness, and user acceptance in extensive applications, particularly notable disparities in multicultural contexts, which require solutions through privacy safeguards, algorithm refinement, and culturally adaptive design. This study is significant as it systematically reveals the potential of affective AI technologies in remote learning, identifies their critical challenges, and offers valuable recommendations and references for future advancement in global education.

Advancing healthcare through mobile collaboration: a survey of intelligent nursing robots research

Mobile collaborative intelligent nursing robots have gained significant attention in the healthcare sector as an innovative solution to address the challenges posed by the increasing aging population and limited medical resources. This article provides a comprehensive overview of the research advancements in this field, covering hospital care, home older adults care, and rehabilitation assistance. In hospital settings, these robots assist healthcare professionals in tasks such as patient monitoring, medication management, and bedside care. For home older adults care, they enhance the older adults sense of security and quality of life by offering daily life support and monitoring. In rehabilitation, these robots provide services such as physical rehabilitation training and social interaction to facilitate patient recovery. However, the development of intelligent nursing robots faces challenges in technology, ethics, law, and user acceptance. Future efforts should focus on improving robots’ perceptual and cognitive abilities, enhancing human-robot interaction, and conducting extensive clinical experiments for broader applications.

An empirical analysis of user intention to use chatbots for airline tickets consultation

Purpose This study aims to empirically analyze the factors influencing users’ intention to use chatbots for airline ticket consultation. It seeks to introduce a comprehensive framework based on the technology acceptance model (TAM) that integrates key factors alongside traditional TAM constructs to understand what drives behavioral intention to use chatbots in the context of airline ticket consultation. Design/methodology/approach The study uses the partial least squares-structural equation modeling (PLS-SEM) approach to validate the proposed model empirically. Data were collected through a survey questionnaire distributed to potential users in Saudi Arabia, with 393 valid responses from a total of 409 received being included in the analysis. Findings The empirical analysis confirms the significance of perceived usefulness and user satisfaction as direct determinants of behavioral intention. Additionally, it reveals that factors such as perceived ubiquitous access, perceived completeness, perceived accuracy, perceived unbiased response and perceived convenience have both direct and indirect significant impacts on the behavioral intention to use chatbots for airline ticket consultation. Originality/value This research advances theoretical understanding and holds practical implications for designing and implementing effective chatbot services. By investigating the complex interplay of these factors, the study makes substantive contributions to both theoretical advancements and practical applications in the field, particularly in enhancing the user experience and acceptance of chatbots for airline ticket consultations.

Analisis Faktor Penerimaan Pengguna ChatGPT dengan Menggunakan Metode TAM pada Mahasiswa Universitas Amikom Purwokerto

In the 5.0 industrial revolution, the field of education has been impacted by the sophistication of technology. The use of ChatGPT, commonly known as AI, greatly assists users in obtaining information, one of which is the use of ChatGPT among students in completing their assignments. This research aims to analyze the factors influencing the acceptance of ChatGPT users using the Technology Acceptance Model (TAM) method among students at Amikom Purwokerto University. The TAM method is used to evaluate two main variables, namely Perceived Usefulness (PU) and Perceived Ease of Use (PEOU), as well as their impact on user attitude (Attitude Toward Using), intention to use (Behavioral Intention to Use), and actual usage by users (Actual System Use). This research uses a quantitative approach with a survey method, where data is collected through questionnaires distributed to students of Universitas Amikom Purwokerto. The analysis results show that PU and PEOU have a significant influence on user attitudes, which affects the intention to use ChatGPT, encouraging users to use ChatGPT more frequently in their activities.

Using neural networks for the detection of textual patterns in academic texts: A case study at the university of Nariño

Dealing manually with large volumes of textual information leads to problems in qualitative research, such as the time spent reading and processing the data and the possibility of losing critical details. In education sciences, investigations are commonly based on the qualitative paradigm, requiring substantial amounts of data to be processed textually. In this situation, an alternative for processing text to detect patterns using neural networks in the computational linguistics scenario is proposed. In this regard, qualitative research using a quasi-experimental method is performed. The quasi-experiment is done within the Qualitative Information Analysis course of the master's degree in Higher Education Teaching in 2022 with Class XIV; in such a course, six students evaluated the computational solution using material related to their working master’s thesis. The results from the application test are satisfactory due to the positive user acceptance, providing a plausible alternative to commercial computational solutions on the market.

COMPARISON OF PROFILE MATCHING AND MOORA METHODS IN DETERMINING LOAN ELIGIBILITY

The objective of this research is to analyze the comparison between the profileimatching method and MOORA in supporting decision-making for loan approvals at the Widya Dharma Student Cooperative (KOPMA). The criteria used in this research include basic salary, length of service, loan duration, membership status, loan amount, and number of dependents. These two methods are compared based on their accuracy levels. The accuracy levels are obtained through testing with the Mean Average Precision (MAP) technique, which measures the accuracy in ranking. The testing is conducted by comparing the ranking results from the method calculations with the rankings from the KOPMA chairman. The analysis results show that the Profile Matching method has a higher accuracy rate, which is 67.83%, compared to the MOORA method, which has an accuracy rate of 45.46%. Besides method testing, system testing was also conducted using the User Acceptance Test (UAT) technique. The UAT results indicate that the developed system aligns with the business processes in determining loan eligibility, the menu layout and contents within the system are well-organized, the system features function properly and are easy to understand, and the system meets expectations.

Unraveling the Dynamics of e-Government Digitization, Penetration and User Experience: A Case Study of Greek Municipalities

This study examines the user experience (UX) of Greek municipal websites, the first in-depth look at local e-Government services in Greece. Using Lighthouse for technical performance and an Adapted UX Audit, it explores factors beyond digital skills affecting citizen adoption of these platforms. Findings reveal widespread deficiencies, with no municipality scoring above 55%, highlighting significant challenges in usability, functionality, and user satisfaction. Critical issues in Technical Performance and Missing Features, both scoring below 50%, suggest that inherent design flaws hinder user acceptance. The study emphasizes the need for improvements in feedback mechanisms, multilingual support, and accessible help sections to better serve diverse populations. Addressing these gaps could enhance e-democracy by fostering more inclusive, user-friendly services. The results advocate for a strategic overhaul of municipal websites to increase engagement, improve e-Government effectiveness, and strengthen democratic participation. Limitations and recommendations for future research and policy-making are also provided.

Integrating Artificial Intelligence in Autonomous Cashier Systems: A Study on Functional Schema Design and Its Impact on Supermarket Operations

This integrative literature review (ILR) explores the transformative potential of artificial intelligence (AI) in optimizing in-store logistics, a crucial aspect for retail environments striving to meet growing consumer demands for convenience and quick product availability. It aims to provide retail managers and logistics specialists with actionable insights on AI-driven tools that enhance competitiveness by improving inventory management and space utilization in retail settings. The conceptual framework is framed by functional schema design, user acceptance and experience, operational efficiency, data security and privacy, and equitable access, emphasizing the ethical and inclusive implementation of technology. Key findings suggest that AI improves demand accuracy, reduces inventory mismatches, and enables adaptable store layouts that respond dynamically to real-time consumer behavior, creating a more efficient, responsive, and customer-centered retail environment. Additionally, this study examines the adoption of AI in cashier systems as a response to evolving consumer expectations for efficient digital shopping experiences, revealing that AI-enabled cashier systems significantly reduce wait times and operational costs while providing personalized experiences through real-time analytics. However, challenges such as robust data security protocols, inclusive design for diverse user groups, and employee reskilling remain critical as automation progresses. Recommendations focus on privacy-enhancing data practices, user-friendly interfaces, and hybrid solutions combining human support with automation to foster inclusivity and consumer trust. Future research should address the long-term impacts of AI in retail, cultural adaptability, customer engagement, and secure data handling to support a balanced, customer-centered approach to automation in the retail sector.

Development and Implementation of the “Keluarga Carade” App: Optimising Information Technology-Based Stunting Surveys to Support Public Health Efforts

Stunting represents a persistent malnutrition issue that impedes children's physical and cognitive development, with potentially lasting impacts throughout their lives. This study aims to develop and implement the Keluarga Carade app, a real-time information technology-based stunting survey system intended to assist surveyors in collecting public health data in Jeneponto Regency. The app was constructed using the Laravel framework and MySQL for data storage, offering features such as family data management, prospective bride data management, questionnaire data input, survey result evaluation, and coordinate point visualization. The study adopted the Unified Theory of Acceptance and Use of Technology (UTAUT) model to evaluate user acceptance, focusing on four variables: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC). Data collection was carried out using a Likert-scale questionnaire with a sample of 32 public health surveyors selected through purposive sampling, and the data were subsequently analysed for validity, reliability, and correlation. The findings confirmed the validity and reliability of all variables, with strong positive correlations observed, highlighting the app’s ease of use, functionality, and effectiveness in supporting stunting survey activities. The Keluarga Carade app demonstrates the potential to optimise stunting monitoring by facilitating efficient data collection and analysis, thus supporting public health efforts to combat stunting. Future research may explore the app’s long-term impact on reducing stunting rates and its integration within broader public health frameworks.

User Behavior and Satisfaction in AI-Generated Video Tools: Insights from Surveys and Online Comments

Abstract. With the rapid advancement of AI, AI-generated video tools have become essential in digital content creation. However, there are significant differences in user acceptance and satisfaction with these tools. This study aims to explore the underlying reasons for these differences by analyzing user behavior, satisfaction levels, and preference characteristics through a questionnaire survey and online review data. Ridge regression analysis identified usage frequency and data security concerns as key factors influencing satisfaction, with familiarity and education level also playing significant roles. Based on these factors, K-means clustering categorized users into three groups: occasional users with high satisfaction, functionality-focused users with the highest satisfaction, and frequent users with lower satisfaction due to data security concerns. Sentiment analysis and LDA topic modeling of online reviews for five AI video tools-Animoto, Invideo, Lumen5, Pictory, and Synthesia-revealed differences in user evaluations. The findings indicate that Lumen5 excels in social media content creation, while Pictory has a weaker user experience with more negative feedback. The study concludes that developers should focus on increasing tool usage frequency and enhancing data security to improve user satisfaction. Moreover, targeted strategies based on different user segments can aid in more precise product optimization and market promotion. This research provides practical guidance for the further development of AI-generated video tools. Future studies could expand the sample size and data dimensions to further validate and deepen these conclusions.

User Behavior and Satisfaction in AI-Generated Video Tools: Insights from Surveys and Online Comments

Abstract. With the rapid advancement of AI, AI-generated video tools have become essential in digital content creation. However, there are significant differences in user acceptance and satisfaction with these tools. This study aims to explore the underlying reasons for these differences by analyzing user behavior, satisfaction levels, and preference characteristics through a questionnaire survey and online review data. Ridge regression analysis identified usage frequency and data security concerns as key factors influencing satisfaction, with familiarity and education level also playing significant roles. Based on these factors, K-means clustering categorized users into three groups: occasional users with high satisfaction, functionality-focused users with the highest satisfaction, and frequent users with lower satisfaction due to data security concerns. Sentiment analysis and LDA topic modeling of online reviews for five AI video tools-Animoto, Invideo, Lumen5, Pictory, and Synthesia-revealed differences in user evaluations. The findings indicate that Lumen5 excels in social media content creation, while Pictory has a weaker user experience with more negative feedback. The study concludes that developers should focus on increasing tool usage frequency and enhancing data security to improve user satisfaction. Moreover, targeted strategies based on different user segments can aid in more precise product optimization and market promotion. This research provides practical guidance for the further development of AI-generated video tools. Future studies could expand the sample size and data dimensions to further validate and deepen these conclusions.

Commercial Wearables for the Management of People with Autism Spectrum Disorder: A Review.

Autism Spectrum Disorder (ASD) necessitates comprehensive management, addressing complex challenges in social communication, behavioral regulation, and sensory processing, for which wearable technologies offer valuable tools to monitor and support interventions. Therefore, this review explores recent advancements in wearable technology, categorizing devices based on executive function, psychomotor skills, and the behavioral/emotional/sensory domain, highlighting their potential to improve ongoing management and intervention. To ensure rigor and comprehensiveness, the review employs a PRISMA-based methodology. Specifically, literature searches were conducted across diverse databases, focusing on studies published between 2014 and 2024, to identify the most commonly used wearables in ASD research. Notably, 55.45% of the 110 devices analyzed had an undefined FDA status, 23.6% received 510(k) clearance, and only a small percentage were classified as FDA Breakthrough Devices or in the submission process. Additionally, approximately 50% of the devices utilized sensors like ECG, EEG, PPG, and EMG, highlighting their widespread use in real-time physiological monitoring. Our work comprehensively analyzes a wide array of wearable technologies, including emerging and advanced. While these technologies have the potential to transform ASD management through real-time data collection and personalized interventions, improved clinical validation and user-centered design are essential for maximizing their effectiveness and user acceptance.

Evaluating small-scale eucalyptus growers’ experience of the EVO mobile application: a comparative analysis of quantitative and qualitative assessment techniques

ABSTRACT Mobile applications (apps) have become widely available as decision support tools in agriculture and forestry worldwide. Owing to the above-mentioned development trend, identifying testing methods that can be easily implemented for evaluating mobile app development targeting rural livelihood-related topics is urgently required. This study examined user experiences with a previously launched and published mobile app (EVO) that small-scale eucalyptus growers can utilize to estimate their gross income and net profit from timber sales. The survey incorporated three user experience assessment methods. The study involved two rounds of data collection regarding the mobile app: the first round concerned version 1.0, whereas the second round referred to version 2.0. The aim was to identify the user experience of the EVO app for guiding further software development to improve its suitability among potential user groups, as well as to estimate the feasibility and usability of survey methods for evaluating mobile apps among smallholders and related stakeholders. The results indicated a remarkable improvement in user satisfaction between the two rounds. Using a combination of system usability scale, user acceptance testing, and quality questions is recommended for collecting valuable and useful feedback for the development of mobile apps.

AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors

This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, as well as build trust through the need for visibility and explainability by increasing user acceptance. This study primarily examines the nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy. The results provide practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users. The results are also important to gain insight into how artificial intelligence fits into and combines with decision-making, which can be derived from research when thinking about embedding systems in ethical standards.

An analysis of dialogue repair in virtual assistants.

Conversational user interfaces have transformed human-computer interaction by providing nearly real-time responses to queries. However, misunderstandings between the user and system persist. This study explores the significance of interactional language in dialogue repair between virtual assistants and users by analyzing interactions with Google Assistant and Siri in both English and Spanish, focusing on the assistants' utilization and response to the colloquial other-initiated repair strategy "huh?", which is prevalent as a human-human dialogue repair strategy. Findings revealed ten distinct assistant-generated repair strategies, but an inability to replicate human-like strategies such as "huh?". Despite slight variations in user acceptability judgments among the two surveyed languages, results indicated an overall hierarchy of preference towards specific dialogue repair strategies, with a notable disparity between the most preferred strategies and those frequently used by the assistants. These findings highlight discrepancies in how interactional language is utilized in human-computer interaction, underscoring the need for further research on the impact of interactional elements among different languages to advance the development of conversational user interfaces across domains, including within human-robot interaction.

Analisis Sentimen Terhadap Sistem Informasi Akademik Mahasiswa pada Aplikasi Edlink dengan Metode K-Nearest Neighbor

The Sevima Edlink application is an academic information system that is widely used by educational institutions in Indonesia to manage student academic data and information. Although this application has various useful features, its successful implementation also depends greatly on user satisfaction and acceptance. Therefore, it is important to analyze user sentiment towards these applications to identify existing strengths and weaknesses. This research aims to analyze user sentiment towards the Sevima Edlink application using the K-Nearest Neighbor (K-NN) method. The K-NN method was chosen because of its simplicity and effective ability to classify sentiment data. The data used in this research are reviews from application users collected from various sources. The results of this research used the K-NN method, namely an accuracy value of 94.47%. So it can be said that the K-KNN algorithm can classify data well and correctly.

The Necessity of System Integration Testing (SIT) and User Acceptance Testing (UAT) in Project Lifecycle

SIT and UAT are the major milestones in the project lifecycle. This paper was able to show the importance of such testing phases in helping to identify bugs early through continuous data-driven reporting and monitoring toward the successful execution of a project. Additionally, this session will cover the importance of regression and end-to-end testing, setting target percentages for the completion of test cases, and putting time aside for retesting. Keywords SIT: System Integration Testing, UAT: User Acceptance Testing, Project Life Cycle, Data-Driven Reporting, Monitoring, Bug Detection, Regression Testing, End-to-End Testing, Target Percentages, Retesting

Assessing public a priori acceptance of fully automated vehicles using an extended technology acceptance model and importance-performance analysis

Fully automated vehicles (FAVs) are anticipated to enter the passenger vehicle market soon. Given the uncertainties surrounding user adoption of this emerging technology, research is needed to understand their user acceptance. While most studies on the acceptance of automated vehicles have been conducted in upper-middle-income or high-income developed countries, similar research in middle-income countries is limited. This study aims to evaluate a priori acceptance of FAVs in a middle-income developing country by extending the Technology Acceptance Model (TAM). Trust, subjective norms, perceived safety risk and four decision-making styles (Thoroughness, Hesitancy, Social resistance, and Perfectionism) were included in the extended model. This study aims to evaluate a priori acceptance of FAVs in a middle-income developing country by extending the Technology Acceptance Model (TAM). Trust, subjective norms, perceived safety risk and four decision-making styles (Thoroughness, Hesitancy, Social resistance, and Perfectionism) were included in the extended model. Structural Equation Modeling was applied to confirm model validation by using data from 1026 drivers from different cities in Iran. A multi-group analysis was conducted to assess whether the relationships between model constructs vary across different demographic and background groups. Additionally, an importance-performance analysis was performed to gain a deeper understanding of the factors influencing behavioral intention. The findings of the model highlighted that beyond the original TAM scales, subjective norms were the strongest predictor in explaining drivers' intentions to use FAVs. Initial trust also moderately contributed to explaining user acceptance of FAVs. The results also revealed that among decision-making styles, thoroughness positively affected behavioral intention through trust, while social resistance had an indirect negative effect on intention. The findings showed that effect of the perceived safety risk on behavioral intention through the initial trust was confirmed; however, importance-performance map analysis revealed that a significant improvement was observed in the safety area that could still be obtained. It is suggested that policymakers start promoting the usefulness and ease of use of FAVs through advertisements, social media, public campaigns and autonomous vehicle test ride events to facilitate the adoption of FAVs when available in countries with similar sociocultural contexts.

Assessing the Safety, User Acceptability, Dissemination, and Reach of a Comprehensive Web-Based Resource on Medications for Opioid Use Disorder (MOUD Hub): Protocol for a Development and Usability Study.

Medications for opioid use disorder (MOUD), such as methadone and buprenorphine, are the gold standard for opioid use disorder (OUD) treatment. Owing to various barriers, MOUD access and retention are low in the United States. The internet presents a digital solution to mitigate barriers, but a comprehensive and reliable resource is lacking. We present a user-friendly, web-based resource, the MOUD Hub, that provides reliable information on MOUD. This study aims to assess the safety, acceptability, feasibility of dissemination, and reach of the MOUD Hub using focus groups and advertising on 1 key search engine and 1 social media platform. This protocol describes the development of the MOUD Hub and the descriptive observational feasibility study that will be undertaken. The MOUD Hub uses motivational interviewing principles to guide users through the stages of change. The website provides evidence-based information from national health and substance use agencies, harm reduction organizations, and peer-reviewed literature. First, pilot focus groups with 10 graduate students who have lived experience with OUD will be conducted to provide feedback on safety concerns. Then, focus groups with 20-30 potential MOUD Hub users (eg, people with OUD with and without MOUD experience, friends and family, and health care providers) will be conducted to assess safety, acceptability, reach, and usability. Data will be analyzed using inductive thematic analysis. The website will be advertised on Google and MOUD-specific Reddit forums to assess dissemination, reach, and user acceptability based on the total user volume, sociodemographic characteristics, pop-up survey responses, and 1-year engagement patterns. This information will be collected through Google Analytics. Potential differences between users from Google and Reddit will be assessed. The MOUD Hub will be launched in January 2025. Data collected from 5 focus groups (approximately 30-40 participants) will be used to improve the website before launching it. There is no target sample size for the second stage of the study as it aims to assess dissemination feasibility and reach. Data will be collected for a year, analyzed every 3 months, and used to improve the website. The MOUD Hub offers an innovative theory-based approach, tailored to people with OUD and their family and friends, to increase access to and retention in MOUD treatment in the United States and provides broader harm reduction resources for those not currently in a position to receive treatment or those at risk of resuming illicit opioid use. Findings from this feasibility phase will serve to better tailor the MOUD Hub. After modifying the website based on our findings, we will use a randomized controlled trial to assess its efficacy in increasing MOUD access and retention, contributing to growing research on web-based interventions for OUD. PRR1-10.2196/57065.