User Attitudes Research Articles

Влияние проблемы мухаджирства на историческую память российского общества

Currently, against the background of the desire of individual political actors to falsify history, the study of the phenomenon of historical memory in the political dimension is of particular importance. The article analyzes muhajirism as a factor of historical memory influencing the current state of Russian society. The features of the reflection of the problem of muhajirism in the scientific literature of various historical periods are considered. A content analysis of social network materials (platforms) was conducted, aimed at identifying the attitude of Russian-speaking users to the problem of muhajirism. The main categories of comments reflecting user reactions to the topic of the resettlement of the highlanders are identified. The content analysis showed that the problem of muhajirism fuels nationalistic sentiments of certain categories of citizens, therefore, it can be used as an instrument of pressure on Russian society in order to disunite it. It is noted that there is a demand in society for reliable and accessible information on the topic of muhajirism, in connection with which such studies are of particular relevance.

Antecedents For the Adoption of Telemedicine in India: Scale development and validation

Background: Telemedicine is increasingly recognized as a promising solution to healthcare challenges in India, particularly in remote areas. However, the country's vast population and geographic diversity present significant obstacles to providing accessible and high-quality healthcare services to all citizens. By leveraging technology, telemedicine has the potential to bridge this gap and enable remote delivery of medical services. Objective: This study aims to develop and validate a scale that assesses the factors influencing the adoption of telemedicine in the Indian context. Understanding these factors is crucial for identifying key drivers and barriers to telemedicine adoption in India. Design: Quantitative methods were employed for scale development. The instrument development process involved several stages: factor identification, item generation, pre-testing, pilot testing, and scale validation. A structured questionnaire was administered to healthcare professionals, industry experts, and patients who have used or intend to use telemedicine. Setting: Confirmatory factor analysis and subsequent tests, such as reliability and validity tests were conducted to establish the internal consistency of the scales. These statistical analyses aimed to identify underlying factors and ensure accurate measurement of latent variables that affect the adoption of telemedicine. Results: The study established a robust scale to assess the five key factors, which are the vital explanatory variables in telemedicine adoption in India. These meticulously validated scales, encompassing technology, government policy, user attitudes, societal demand, and healthcare professional perspectives, demonstrate high reliability and validity in understanding the adoption dynamics of telemedicine in the context of India. Conclusion: This research offers a validated scale for assessing telemedicine adoption in India, crucial for healthcare service providers, policy makers, and researchers in this field. It enables informed decisions in implementing telemedicine, addressing unique challenges and opportunities, and significantly contributing to optimizing healthcare delivery across India's diverse socio-economic and geographical landscape.

Evaluation of the effects of avatar on learning temporomandibular joint in a metaverse-based training.

Avatars, representing users in the digital world, can influence users' behavior and attitudes. This study evaluates the impact of representing dental students receiving temporomandibular joint (TMJ) education in the metaverse via an anonymous or identified avatar. Participants included 80 dental students in their fourth and fifth years of study. They were randomly assigned to either the avatar group (identified avatar) or the control group (anonymous avatar). Prior to training, participants completed a demographic questionnaire and a pretraining knowledge assessment. TMJ training was conducted in the metaverse for both groups. Pre- and post-training assessments included the Spielberger State-Trait Anxiety Inventory and a shyness scale to ensure group comparability. A post-test consisting of five questions was administered to both groups after 2 weeks of training. There were no significant differences in pretraining scores for prior knowledge (p=0.67), trait anxiety (p=0.28), state anxiety (p=0.92), or shyness (p=0.42) between the avatar and control groups, indicating comparability at baseline. Post-training analysis revealed significantly higher post-test scores in the avatar group (median=80) compared to the control group (median=60) (p=0.03). Metaverse environments offer various benefits for students, educators, and educational institutions in health education programs. Representing learners and their identities in training environments can enhance learning outcomes.

Understanding User Attitude towards AI Agents: The Roles of Perceived Competence, Trust in Technology, and Social Influence

Understanding User Attitude towards AI Agents: The Roles of Perceived Competence, Trust in Technology, and Social Influence

Advertising Breaks the Privacy of WeChat Circle of Friends Analysis

With the rapid development of Internet technology and the popularity of smart phones, social media has become an indispensable part of People's Daily life. WeChat, one of the largest social media platforms in China, has gained popularity for its moments function due to its high degree of privacy and user viscosity. However, with the rise of the social media advertising market, the frequent appearance of advertising messages in WeChat moments not only disrupts the social experience of users, but also may cause users to worry about privacy leakage. This study aims to deeply analyze how advertisement delivery breaks the privacy of WeChat circle of friends, and explore the possibility of optimizing advertisement delivery strategy, including delivery quantity, delivery characteristics, target audience positioning and so on. Through the optimization strategy, the purpose is to reduce the intrusion of user privacy, improve user experience, and achieve a win-win situation between business interests and user privacy protection. Using questionnaire survey and hot list comparison, the study analyzed the impact of advertising on users' privacy perception, attitude and behavior. Suppose the results show that AD delivery breaks the privacy of WeChat moments to some extent, and users are put off by frequent and non-personalized ads. The study also found that gender, age, occupation and other factors have an impact on the perception of privacy in WeChat circle of friends. Based on the research results, marketing strategies and content suggestions for different user groups are proposed, in order to better safeguard users' privacy rights and social experience while ensuring the effect of advertising delivery and promote the sustainable development of social media platforms and advertising industry.

Effects of cyber-security and trust issues on consumers’ intention to use E-commerce systems in rural South Africa

The adoption and use of ICT have increased drastically in developed countries, but it cannot be said that it lags in developing countries. ICT evolution has introduced e-commerce services for users, but these services fail because of low user acceptance and user attitude, especially in developing nations. The security and trustworthiness of e-commerce are known to be of the most significant concern to people intending to use e-commerce services. The study looked at the role of individual characteristics on how cyber-security and trust issues affect non-users of e-commerce systems' intention to use e-commerce systems in South Africa. The effect of cyber security on consumers’ intentions and actual use of e-commerce systems was examined. This study used a quantitative research approach. The findings revealed that perceived security and perceived trust was the primary concern for non-users of e-commerce intentions. It was shown that e-commerce system users are still concerned about security, even though they intend to continue using e-commerce systems. For the success of e-commerce in rural communities, the government needs to join with retailers and SMEs to start awareness campaigns that will clarify how e-commerce systems work and eradicate negative perceptions of e-commerce systems.

An automated approach to identify sarcasm in low-resource language.

Sarcasm detection has emerged due to its applicability in natural language processing (NLP) but lacks substantial exploration in low-resource languages like Urdu, Arabic, Pashto, and Roman-Urdu. While fewer studies identifying sarcasm have focused on low-resource languages, most of the work is in English. This research addresses the gap by exploring the efficacy of diverse machine learning (ML) algorithms in identifying sarcasm in Urdu. The scarcity of annotated datasets for low-resource language becomes a challenge. To overcome the challenge, we curated and released a comparatively large dataset named Urdu Sarcastic Tweets (UST) Dataset, comprising user-generated comments from [Formula: see text] (former Twitter). Automatic sarcasm detection in text involves using computational methods to determine if a given statement is intended to be sarcastic. However, this task is challenging due to the influence of the user's behavior and attitude and their expression of emotions. To address this challenge, we employ various baseline ML classifiers to evaluate their effectiveness in detecting sarcasm in low-resource languages. The primary models evaluated in this study are support vector machine (SVM), decision tree (DT), K-Nearest Neighbor Classifier (K-NN), linear regression (LR), random forest (RF), Naïve Bayes (NB), and XGBoost. Our study's assessment involved validating the performance of these ML classifiers on two distinct datasets-the Tanz-Indicator and the UST dataset. The SVM classifier consistently outperformed other ML models with an accuracy of 0.85 across various experimental setups. This research underscores the importance of tailored sarcasm detection approaches to accommodate specific linguistic characteristics in low-resource languages, paving the way for future investigations. By providing open access to the UST dataset, we encourage its use as a benchmark for sarcasm detection research in similar linguistic contexts.

PENINGKATAN MUTU DAN EFISIENSI PELAYANAN KESEHATAN MELALUI IMPLEMENTASI REKAM MEDIS ELEKTRONIK DI RUMAH SAKIT HASTA HUSADA

Implementation of electronic medical records (RME) in hospitals has become an important step in improving the quality and efficiency of health services. Hasta Husada Hospital has adopted this system, but still faces various challenges. This study aims to analyze and evaluate improving the quality and efficiency of health services through the implementation of electronic medical records (RME) at Hasta Husada Hospital. This research uses a qualitative approach. The research results show that RME provides significant benefits, including fast and accurate access to patient data, increased work efficiency, and reduced medical errors. Although most users feel comfortable using RME after training, there are still complaints about the system interface needing to be simplified. User attitudes were generally positive towards RME after seeing real benefits, despite initial resistance. Implementation of the Hospital Management Information System (SIMRS) has shown mixed results, with some units having fully transitioned to RME and others still in the transition stage.

E-Wallets Unplugged: Navigating Benefits and Challenges in a Post-Pandemic World

The COVID-19 pandemic has expedited the utilization of digital payment alternatives, with e-wallets becoming a significant component of the financial ecosystem. This study examines the diverse benefits and challenges of e-wallets in a post-pandemic context. The researchers conducted a systematic literature review (SLR) of peer-reviewed academic papers utilizing the Emerald Insights database. Forty-four articles published between January 2021 and September 2024 were reviewed, highlighting the study's benefits and challenges. The findings indicate that e-wallets enhance financial inclusion, promote convenience and accessibility, stimulate business and economic growth, support the digital economy, facilitate technological integration, and improve consumer convenience, especially in an increasingly cashless environment. Nonetheless, the study also examines the substantial challenges associated with this swift digital transformation, encompassing security issues, poor adoption rates, insufficient knowledge, trust deficits, regulatory hurdles, feature limitations, and user attitude challenges. This study seeks to elucidate the changing function of e-wallets in influencing the future of commerce through a thorough investigation. The study offers recommendations for e-wallet providers, policymakers, and researchers to successfully manage the complexity of e-wallet adoption, enhancing benefits while resolving the inherent challenges.

Niat Terus Menggunakan e-Wallet pada Generasi Z di Bali: Peran Nilai Utilitarian dan Gamifikasi

The development of e-wallet technology has become a significant phenomenon for Generation Z, who grew up in the digital era. Practical values and gamification influence the acceptance and use of e-wallets in these applications. This study aims to understand how valuable values ​​and gamification influence attitudes and continued intention to use e-wallets in Generation Z. This study uses a quantitative approach with a sample of 140 e-wallet users in Bali, using a purposive sampling technique. Data were collected through questionnaires and analyzed using Structural Equation Modeling (SEM) with SmartPLS software. The analysis results show that utilitarian values ​​have a positive and significant effect on attitudes and continued intention to use e-wallets in Generation Z. Conversely, the effect of gamification on continued intention to use is not essential. However, it has a positive impact on user attitudes towards e-wallets. This finding implies the importance of considering utilitarian values ​​in e-wallet development to increase user adoption and retention. While gamification can improve user attitudes towards e-wallets, its effect on continued intention to use is more complex and not always significant. This research provides insight into the factors influencing e-wallet acceptance among Generation Z, highlighting the importance of marketing and development strategies that focus on efficiency and convenience of use and the integration of gamification elements that suit young users' preferences.

Modeling perception towards sustainable ferries through ridership surveys: A case study of Michigan islands with policy implications

Many island communities rely on ferries as their sole mode of transport to access healthcare, education, and economic opportunities, aside from region connectivity. With sizeable ferry operations throughout the country, the carbon footprint of marine transportation, including passenger ferries, cannot be overlooked. Discussions of environmental sustainability have gained considerable momentum in recent years, especially in decarbonizing the transportation industry. While many studies have explored sustainable ferry operations from the lens of operators, users’ perspectives are less explored. Users’ expectations, experiences, socio-demographic heterogeneity, and sensitivity to pricing may correlate with their opinions on environmental sustainability of ferry services and need to be understood. This study delves into the nuanced relationships between users’ profiles and their willingness to support sustainable ferry initiatives through a comprehensive survey across four major islands in Michigan and utilizing Structural Equation Modeling (SEM). Key findings indicate that negative experiences drastically impact user satisfaction and attitudes toward sustainability, highlighting the crucial role of operational reliability in fostering support for environmentally friendly upgrades. Furthermore, the study reveals significant disparities in sustainability support between tourists and residents, with demographic factors like gender and income significantly shaping sustainability preferences. This underscores the need for targeted communication, policy framing strategies, and consideration of socio-economic ramifications that address these disparities to ensure the successful implementation of sustainable practices in ferry operations This research aids policymakers in crafting targeted interventions that ensure the dual goals of environmental sustainability and mobility justice are met. Overall, this research presents a practical framework for evaluating policy impacts on ferry service users, with implications extending beyond the Michigan islands to similar ferry-dependent communities.

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.

Analysis of willingness to use health management APP for female college students: application of UTAUT model based on Fogg theory.

As the development process of medical industry informatization has entered the stage of smart healthcare, health management applications (apps) have played an important role in improving people's health and preventing diseases, especially among female college students. This study combines the UTAUT model and the Fogg behavioral model (FBM) as a theoretical framework to investigate the factors affecting female college students' willingness to use health management apps. A survey was conducted with 624 female college students regarding their usage of AI health management mobile applications. The analysis reveals that social influence (β = 0.497, p < 0.001), performance expectancy (β = 0.268, p < 0.001), effort expectancy (β = 0.359, p < 0.001), and facilitating conditions (β = 0.603, p < 0.001) positively predict attitude; social influence (β = 0.36, p < 0.001) and effort expectancy (β = 0.183, p < 0.001) positively predict perceived risk, while facilitating conditions negatively predict perceived risk (β = -0.108, p < 0.01). Additionally, performance expectancy (β = 0.231, p < 0.001), effort expectancy (β = 0.285, p < 0.001), facilitating conditions (β = 0.25, p < 0.01), and attitude (β = 0.291, p < 0.05) positively predict an individual's intention to use such applications, which in turn affects actual behavior (β = 0.804, p < 0.001). This study develops a comprehensive theoretical framework to explore the psychological and social factors influencing female college students' utilization of health management applications. The findings underscore the significant roles of social influence, performance expectancy, effort expectancy, and facilitating conditions in shaping user attitudes and intentions. These insights offer valuable guidance for formulating effective interventions to enhance the adoption of these applications.

How food delivery service can be sustainable: some evidence from the Giusta delivery case

PurposeThe study explores the drivers of the intention to continue using sustainable food delivery services, considering the influence of value, satisfaction and the combined effects of utilitarian and hedonic motives. We employ a comprehensive framework that integrates the norm activation model (NAM) and the theory of planned behavior (TPB), with satisfaction, utilitarian motives and hedonic motives. This approach facilitates a deeper understanding of the factors motivating users to remain loyal to the service.Design/methodology/approachThe study examines the data using a quantitative approach based on the partial least squares approach to structural equation modeling (PLS-SEM).FindingsThe findings suggest that the intention to continue using sustainable food delivery services, such as the Giusta app, is significantly influenced by user satisfaction and attitudes. Attitudes not only influence the intention but also user satisfaction and are shaped by utilitarian motivations associated with sustainable food delivery. Beyond personal expectations and perceived benefits, the study reveals that behavior is also driven by altruistic values and moral convictions. Specifically, attitudes and satisfaction are positively affected by personal norms, which are influenced by ascribed responsibility. This ascribed responsibility is, in turn, shaped by awareness of both environmental and social issues.Originality/valueThis research contributes to the current debate on consumers' ethical behavior in food delivery. Only a few studies have integrated the NAM and TPB models in this context. This article highlights the factors useful in predicting people’s choice of food delivery services, showing the key role of personal norms, attitude, satisfaction and utilitarian motives.

Factors Influencing Sustained Use of Shared E-Scooter Services in Urban Taiwan

This study investigates the factors that drive users to sustain their usage of shared electric scooter (e-scooter) services in Taiwan, distinguishing itself from the conventional focus on predicting consumers’ initial adoption and behavioural intentions. It employs subjective rating questions, incorporating constructs related to user acceptance, attitudes and user experience (UX). Through hierarchical regression analysis of quantitative survey data, the study identifies key factors such as users’ modes of transportation, environmental attitudes, acceptance of shared services, attitudes towards private scooters, UX, total usage instances and age. However, reliance on private scooters as a mode of transportation and frequent usage of shared e-scooters negatively impact the sustained usage of these services. The research further highlights early development challenges in shared vehicle services, including concerns over personal data security, user-unfriendly system designs, lack of convenience, inadequate parking infrastructure and ineffective financial incentives. Based on these findings, the study provides recommendations for service providers and government entities to enhance service design and proactively address these challenges. Implementing these recommendations is expected to mitigate the impact of these challenges and potentially improve user acceptance, UX, and the overall sustainability of shared vehicle services.

Measuring Multi-Dimensional Mobile Behavior Effect on Inclusive Finance: Evidence from China

Credit Invisible is one key area that many countries put much effort to solve in decades. According to the 2020 World Bank statistics, for example, there are over 500 million Chinese and 45 million American, classified as credit invisible who don’t have banking and finance history in bank or credit bureau, making them difficult to borrow money from financial institution. Previous studies adopted different non-financial information to evaluate one’s credit worthiness and status to address this issue. However, they provide little information about how real mobile user interactions can be used to solve this issue in inclusive finance. This paper proposes a novel data generative framework to fusion APP data, call detail record data and SMS data with a total of 4,689 attributes derived from a large-scale mobile dataset. We then construct a unique set of mobile behavior-driven credit risk factors based on statistical diversity, intensity, consistency, and regularity of mobile user behavior characterizing user preferences, attitudes, geolocation, and temporal patterns. Empirical analysis demonstrates that the newly discovered mobile behavior factors are useful as new inputs for credit scoring and proves the factors representing new source of positive and negative credit information. Decision tree analysis and Quantile regression are conducted to validate effect of these factors to credit default. It facilitates credit assessment based on non-financial data for the credit invisible people, which promoting inclusive finance to larger community in society. We also analyze implications of mobile user characterization findings in relation to credit default which helps decision makers to optimize credit policy and product design.

The physician-AI relationship: partnering for precision medicine via clinical decision support

Abstract Background Clinical decision support systems (CDSSs) can enhance physician decision-making, but successful adoption depends on positive user attitudes. Understanding Israeli physicians’ attitudes toward CDSSs is crucial for overcoming implementation barriers and refining training. Methods Semi-structured interviews explored knowledge, experiences, perceived benefits/drawbacks, and potential barriers/facilitators to CDSS use among 25 Israeli physicians across specialties, roles, settings, and experience levels selected via stratified sampling. Individual 45-minute video interviews were conducted in January-July 2023. Results Familiarity and CDSS experience varied substantially across medical specialties. While risk calculators, information systems, and expert systems disseminating contemporary research insights are widespread, AI-based system adoption is relatively low outside radiology. Physicians expressed positive attitudes, recognizing CDSSs’ potential to enhance clinical work, reduce errors, and alleviate burdens. However, concerns included AI systems’ impact on workload, patient relationships, skill erosion, and overreliance. Key implementation barriers involved technological infrastructure, organizational processes, physician perceptions, and system features. Facilitating factors included tailored design, focused training, continuous accuracy/reliability validation, and clear autonomy/liability guidelines. Conclusions Addressing key concerns around transparency, trustworthiness and responsible use is crucial for AI-CDSS to fulfill its promise of enhancing quality and outcomes. Insights from Israeli clinicians provide guidance on priorities like transparency of data and algorithms as well as ensuring AI behaves ethically. Collaboration among technology designers, healthcare professionals and stakeholders can help develop systems that physicians trust to improve care while safeguarding patients. Ongoing evaluation is also needed to optimize AI-CDSS over time. Key messages • Understanding physician attitudes is key to overcoming barriers to clinical decision support system adoption and optimizing training protocols. • Addressing transparency, trustworthiness, and ethical considerations around AI clinical decision support systems is crucial for enhancing quality care.

Wearable neurofeedback acceptance model for students’ stress and anxiety management in academic settings

This study investigates the technology acceptance of a proposed multimodal wearable sensing framework, named mSense, within the context of non-invasive real-time neurofeedback for student stress and anxiety management. The COVID-19 pandemic has intensified mental health challenges, particularly for students. Non-invasive techniques, such as wearable biofeedback and neurofeedback devices, are suggested as potential solutions. To explore the acceptance and intention to use such innovative devices, this research applies the Technology Acceptance Model (TAM), based on the co-creation approach. An online survey was conducted with 106 participants, including higher education students, health researchers, medical professionals, and software developers. The TAM key constructs (usage attitude, perceived usefulness, perceived ease of use, and intention to use) were validated through statistical analysis, including Partial Least Square-Structural Equation Modeling. Additionally, qualitative analysis of open-ended survey responses was performed. Results confirm the acceptance of the mSense framework for neurofeedback-based stress and anxiety management. The study contributes valuable insights into factors influencing user intention to use multimodal wearable devices in educational settings. The findings have theoretical implications for technology acceptance and practical implications for extending the usage of innovative sensors in clinical and educational environments, thereby supporting both physical and mental health.

The Determination of User Retention to Use e-Wallet after COVID-19 Pandemic (Case Study of G-Wallet in Thailand)

The determinants of G-Wallet user retention has become important. This study examines the factors influencing user retention and the continued use of the G-Wallet application in Thailand. This research investigates the relationships between perceived usefulness, perceived ease of use, satisfaction, confirmation of expectation, relative advantage, compatibility, complexity, behavioral intention, and continuous use behavior. This research is quantitative research with 573 users. Key findings indicate that PU significantly enhances positive user attitudes towards G-Wallet, while PEoU has a negative effect on attitude, suggesting a complex relationship where ease of use may be perceived as diminishing the value of the application. COE positively impacts user satisfaction; however high satisfaction does not directly translate to a strong behavioral intention for continued use. RA and COMP positively affect behavioral intention, while complexity acted as a deterrent. Behavioral intention strongly predicts continuous use behavior, though satisfaction’s effect on continued use is significant.

Oral Diabetes Medication Videos on Douyin: Analysis of Information Quality and User Comment Attitudes.

Oral diabetes medications are important for glucose management in people with diabetes. Although there are many health-related videos on Douyin (the Chinese version of TikTok), the quality of information and the effects on user comment attitudes are unclear. The purpose of this study was to analyze the quality of information and user comment attitudes related to oral diabetes medication videos on Douyin. The key phrase "oral diabetes medications" was used to search Douyin on July 24, 2023, and the final samples included 138 videos. The basic information in the videos and the content of user comments were captured using Python. Each video was assigned a sentiment category based on the predominant positive, neutral, or negative attitude, as analyzed using the Weiciyun website. Two independent raters assessed the video content and information quality using the DISCERN (a tool for assessing health information quality) and PEMAT-A/V (Patient Education Materials Assessment Tool for Audiovisual Materials) instruments. Doctors were the main source of the videos (136/138, 98.6%). The overall information quality of the videos was acceptable (median 3, IQR 1). Videos on Douyin showed relatively high understandability (median 75%, IQR 16.6%) but poor actionability (median 66.7%, IQR 48%). Most content on oral diabetes medications on Douyin related to the mechanism of action (75/138, 54.3%), precautions (70/138, 50.7%), and advantages (68/138, 49.3%), with limited content on indications (19/138, 13.8%) and contraindications (14/138, 10.1%). It was found that 10.1% (14/138) of the videos contained misinformation, of which 50% (7/14) were about the method of administration. Regarding user comment attitudes, the majority of videos garnered positive comments (81/138, 58.7%), followed by neutral comments (46/138, 33.3%) and negative comments (11/138, 8%). Multinomial logistic regression revealed 2 factors influencing a positive attitude: user comment count (adjusted odds ratio [OR] 1.00, 95% CI 1.00-1.00; P=.02) and information quality of treatment choices (adjusted OR 1.49, 95% CI 1.09-2.04; P=.01). Despite most videos on Douyin being posted by doctors, with generally acceptable information quality and positive user comment attitudes, some content inaccuracies and poor actionability remain. Users show more positive attitudes toward videos with high-quality information about treatment choices. This study suggests that health care providers should ensure the accuracy and actionability of video content, enhance the information quality of treatment choices of oral diabetes medications to foster positive user attitudes, help users access accurate health information, and promote medication adherence.