User Trust Research Articles

Privacy, confidentiality and ethical concerns in audio ai assistants: A comparative study of North American, European, and Asian Markets

This study examines the ethical, privacy, and confidentiality issues with audio AI assistants in the markets of North America, Europe, and Asia. Understanding the ramifications of data collecting, storage, and usage is crucial as the use of technologies like Google Assistant and Amazon Alexa grows. The study highlights significant regulatory differences between the areas; North America offers a fragmented landscape, and many Asian nations are still developing their data protection legislation, while Europe leads with strong frameworks like the General Data Protection Regulation (GDPR). Key challenges identified include a lack of user awareness regarding privacy rights, extensive data collection practices, unauthorized listening, data breaches, and cultural differences influencing attitudes toward data protection. The findings reveal that these challenges impact user trust and hinder the responsible innovation of AI technologies. Recommendations for improvement include establishing global regulations to promote consistency in data protection, adopting best practices such as privacy-by-design principles, and enhancing user education initiatives. Additionally, the study identifies critical areas for future research, particularly the role of AI assistants in sensitive sectors like healthcare and education, where privacy concerns may differ significantly. Overall, this comparative analysis provides a foundational understanding of audio AI assistants' ethical landscape and underscores the importance of addressing these issues to foster a secure and trustworthy digital environment.

Trusting as an Emergent: Implications for Design

The quest for an adequate model of trust in AI systems has led to rosters of influencing factors and proposals for how trust can be “designed-in.” An alternative is to consider trusting as an emergent. While progressive maturation of user trust may be desired, it is the exception. Trust can arise rapidly, but it can diminish in a heartbeat. It can be orthogonal to reliance. Trusting and relying are context-contingent. Work-centered design principles can make trustability achievable, but it can only be realized through user experience. And principles for “designing-in” trust do not span the creativity gap.

Trustworthiness of EHRs: Key Factors from a User-Centered Study in Germany

Abstract Background The integration of digital technologies, particularly Electronic Health Records (EHRs), promises substantial improvements in patient care and cost reductions. However, the success of EHRs depends heavily on patient trust and usage, which are influenced by their control over data visibility, storage, and sharing. Identifying the factors that enhance the perceived trustworthiness of EHRs is essential for increasing their reliability and acceptance in healthcare settings. Methods Participants (N = 30) first interacted with an interactive EHR prototype, then evaluated its trustworthiness and suggested improvements during semi-structured interviews. These interviews were transcribed and analyzed using thematic analysis to identify key themes affecting perceptions of the EHR’s trustworthiness. Results Through thematic analysis, we developed a categorical system encompassing five principal themes: provider reputation, information quality, system characteristics, user control, and user feedback. The analysis revealed that deficiencies in design and usability indirectly affect perceptions of privacy and data security. Conclusions Our study underscores the critical role of contextual factors-including provider reputation, strategic IT partnerships, and user feedback-in cultivating trust in Electronic Health Record (EHR) systems. Addressing privacy concerns and bolstering user trust necessitates not only a professional approach to design and usability but also clear communication about adherence to rigorous data privacy and security standards. In Germany’s tightly regulated environment, clearly communicating EHR security standards is crucial. Enhancing trust also involves robust customer support, user control over their data, and leveraging social proof like high download rates or positive reviews. These approaches could significantly improve EHR acceptance and trust in digital health across the country. Key messages • The perceived trustworthiness of EHRs is essential for increasing their reliability and acceptance in healthcare settings. • Effective communication and design are critical for building trust in digital health technologies. Trust in EHRs grows with transparent data policies and empowering user controls.

Exploring the mechanism of sustained consumer trust in AI chatbots after service failures: a perspective based on attribution and CASA theories

In recent years, artificial intelligence (AI) technology has been widely employed in brand customer service. However, the inherent limitations of computer-generated natural language content occasionally lead to failures in human-computer interactions, potentially damaging a company’s brand image. Therefore, it is crucial to explore how to maintain consumer trust after AI chatbots fail to provide successful service. This study constructs a model to examine the impact of social interaction cues and anthropomorphic factors on users’ sustained trust by integrating the Computers As Social Actors (CASA) theory with attribution theory. An empirical analysis of 462 survey responses reveals that CASA factors (perceived anthropomorphic characteristics, perceived empathic abilities, and perceived interaction quality) can effectively enhance user trust in AI customer service following interaction failures. This process of sustaining trust is mediated through different attributions of failure. Furthermore, AI anxiety, as a cognitive characteristic of users, not only negatively impacts sustained trust but also significantly moderates the effect of internal attributions on sustained trust. These findings expand the research domain of human-computer interaction and provide insights for the practical development of AI chatbots in communication and customer service fields.

Explainability increases trust resilience in intelligent agents.

Even though artificial intelligence (AI)-based systems typically outperform human decision-makers, they are not immune to errors, leading users to lose trust in them and be less likely to use them again-a phenomenon known as algorithm aversion. The purpose of the present research was to investigate whether explainable AI (XAI) could function as a viable strategy to counter algorithm aversion. We conducted two experiments to examine how XAI influences users' willingness to continue using AI-based systems when these systems exhibit errors. The results showed that, following the observation of algorithms erring, the inclination of users to delegate decisions to or follow advice from intelligent agents significantly decreased compared to the period before the errors were revealed. However, the explainability effectively mitigated this decline, with users in the XAI condition being more likely to continue utilizing intelligent agents for subsequent tasks after seeing algorithms erring than those in the non-XAI condition. We further found that the explainability could reduce users' decision regret, and the decrease in decision regret mediated the relationship between the explainability and re-use behaviour. These findings underscore the adaptive function of XAI in alleviating negative user experiences and maintaining user trust in the context of imperfect AI.

Geopolitics, Privacy, and User Trust: TikTok

The paper uses a thorough review of the current literature, governmental materials, and information available on ByteDance as the major source for analyzing the link between geopolitical aspects, privacy concerns, and user engagement with businesses. The study found that the uncertainty surrounding TikTok’s data usage and the ongoing tensions between China and the United States leads to a decline in user trust. This is particularly evident in the e-commerce sector, where individual privacy concerns directly affect customers’ buying habits and commercial involvement. This phenomenon would directly impact TikTok’s international e-commerce expansion, as well as user engagement on the platform; ultimately affect the platform’s overall growth and revenue potential. The paper concludes with recommendations for enhancing data privacy on TikTok such as reducing data collection from minors, putting stricter regulations on third-party data access and being more transparent about their data policies.

Riding the FinTech Wave: Exploring Service Satisfaction and Trust among Generations Y and Z

Financial technology, or FinTech, is revolutionizing banking worldwide by introducing innovative services. However, clients' comprehension and willingness to embrace or adapt to new technologies remain uncertain. As FinTech continues to disrupt traditional banking procedures, it is critical to investigate how these innovations affect Generation Y and Z's perceptions of service satisfaction. As a result, the objective of this study is to determine the major characteristics that impact client satisfaction and trust in the use of FinTech-based services. Data from 250 respondents who use FinTech services show that User Trust and User Satisfaction are important for them when adopting FinTech. The results indicate that security, privacy, ease of use, and convenience are the key factors positively influencing user trust and satisfaction in FinTech services, with a reciprocal relationship between trust and satisfaction. This symbiotic relationship highlights the importance for FinTech companies to prioritize both customer satisfaction and trust-building initiatives to foster long-term relationships with their users

Perceived Ease of Use and Perceived Usefulness, Intention to Use Digital Banks and the Role of Trust as Mediating Variables

By incorporating the trust variable as a mediator in the relationship between perceived ease of use and perceived usefulness of using digital banking services on intention to use digital banking services and location—research in the special region of Yogyakarta—this study aims to analyze banking customers’ tendencies to use digital banking services using the technology acceptance model (TAM) theoretical approach. This research’s type and source of data is quantitative with primary data. Questionnaires were distributed as part of a survey procedure to collect data for this study. Using sample data from 208 respondents and the structural equation model (SEM) analysis tool, he concluded that while users’ perceptions of the usefulness and ease of use of digital banking services have no bearing on each other, their perceptions of the ease of use of these services has a significant impact on their trust in them. It has a significant impact on user trust in digital banking services, which in turn has a significant impact on users’ intention to use digital banking services; perceived ease of use of digital banking services has no significant impact on users’ intention to use digital banking services; on the other hand, perceived usefulness of digital banking services has a significant impact on users’ intention to use digital banking services; trust in digital banking services mediates the relationship between users’ intention to use digital banking services and perceived ease of use of digital banking services. Keywords: perceived ease of use, perceived usefulness, trust, intention to use, digital bank

The Impact of Blockchain Technology on E-commerce Product Development: Case Studies of Walmart and LVMH

In the current digital era, the emergence of blockchain technology has profoundly impacted business models, especially in e-commerce. Blockchain technology brings new opportunities and challenges to product sales, transactions, and management. This study examines the impact of blockchain technology on the development of e-commerce products, particularly through the cases of Walmart and Louis Vuitton (LVMH). It demonstrates the application of blockchain and its effects on product traceability, anti-counterfeiting verification, data transparency and privacy protection. It was found that blockchain technology improves data security and user trust, reduces transaction costs, and enhances supply chain management efficiency. However, blockchain also needs help regarding energy consumption, performance bottlenecks, privacy protection, inconsistent technical standards, and unclear laws and regulations. It is necessary to take appropriate measures to reduce risks and ensure the effective application of technology. This study concludes with a series of recommendations to promote the effective application of blockchain technology in e-commerce to facilitate the e-commerce industry's sustainable development.

Design and Development of a Smart Fidget Toy Using Blockchain Technology to Improve Health Data Control.

This study explores the integration of blockchain technology in wearable health devices through the design and development of a Smart Fidget Toy. We aimed to investigate design challenges and opportunities of blockchain-based health devices, examine the impact of blockchain integration user experience, and assess its potential to improve data control and user trust. Using an iterative user-centered design approach, we developed a mid-fidelity prototype of a physical fidget device with a blockchain-based web application. Our key contributions include the design of a fidget toy using blockchain for secure health data management, an iterative development process balancing user needs with blockchain integration challenges, and insights into user perceptions of blockchain wearables for health. We conducted user studies, including a survey (n = 28), focus group (n = 6), interactive wireframe testing (n = 7), and prototype testing (n = 10). Our study revealed high user interest (70%) in blockchain-based data control and sharing features and improved perceived security of data (90% of users) with blockchain integration. However, we also identified challenges in user understanding of blockchain concepts, necessitating additional support. Our smart contract, deployed on the Polygon zkEVM testnet, efficiently manages data storage and retrieval while maintaining user privacy. This research advances the understanding of blockchain applications in health wearables, offering valuable insights for the future development of this field.

AI-Generated Spam Review Detection Framework with Deep Learning Algorithms and Natural Language Processing

Spam reviews pose a significant challenge to the integrity of online platforms, misleading consumers and undermining the credibility of genuine feedback. This paper introduces an innovative AI-generated spam review detection framework that leverages Deep Learning algorithms and Natural Language Processing (NLP) techniques to identify and mitigate spam reviews effectively. Our framework utilizes multiple Deep Learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), to capture intricate patterns in textual data. The system processes and analyzes large volumes of review content to detect deceptive patterns by utilizing advanced NLP and text embedding techniques such as One-Hot Encoding, Word2Vec, and Term Frequency-Inverse Document Frequency (TF-IDF). By combining three embedding techniques with four Deep Learning algorithms, a total of twelve exhaustive experiments were conducted to detect AI-generated spam reviews. The experimental results demonstrate that our approach outperforms the traditional machine learning models, offering a robust solution for ensuring the authenticity of online reviews. Among the models evaluated, those employing Word2Vec embeddings, particularly the BiLSTM_Word2Vec model, exhibited the strongest performance. The BiLSTM model with Word2Vec achieved the highest performance, with an exceptional accuracy of 98.46%, a precision of 0.98, a recall of 0.97, and an F1-score of 0.98, reflecting a near-perfect balance between precision and recall. Its high F2-score (0.9810) and F0.5-score (0.9857) further highlight its effectiveness in accurately detecting AI-generated spam while minimizing false positives, making it the most reliable option for this task. Similarly, the Word2Vec-based LSTM model also performed exceptionally well, with an accuracy of 97.58%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. The CNN model with Word2Vec similarly delivered strong results, achieving an accuracy of 97.61%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. This study is unique in its focus on detecting spam reviews specifically generated by AI-based tools rather than solely detecting spam reviews or AI-generated text. This research contributes to the field of spam detection by offering a scalable, efficient, and accurate framework that can be integrated into various online platforms, enhancing user trust and the decision-making processes.

Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transparency and trust of complex DL methods. However, XAI systems face challenges in gaining acceptance within the healthcare sector, mainly due to technical hurdles in utilizing these systems in practice and the lack of human-centered evaluation/validation. In this study, we focus on visual XAI systems applied to DL-enabled diagnostic system in chest radiography. In particular, we conduct a user study to evaluate two prominent visual XAI techniques from the human perspective. To this end, we created two clinical scenarios for diagnosing pneumonia and COVID-19 using DL techniques applied to chest X-ray and CT scans. The achieved accuracy rates were 90% for pneumonia and 98% for COVID-19. Subsequently, we employed two well-known XAI methods, Grad-CAM (Gradient-weighted Class Activation Mapping) and LIME (Local Interpretable Model-agnostic Explanations), to generate visual explanations elucidating the AI decision-making process. The visual explainability results were shared through a user study, undergoing evaluation by medical professionals in terms of clinical relevance, coherency, and user trust. In general, participants expressed a positive perception of the use of XAI systems in chest radiography. However, there was a noticeable lack of awareness regarding their value and practical aspects. Regarding preferences, Grad-CAM showed superior performance over LIME in terms of coherency and trust, although concerns were raised about its clinical usability. Our findings highlight key user-driven explainability requirements, emphasizing the importance of multi-modal explainability and the necessity to increase awareness of XAI systems among medical practitioners. Inclusive design was also identified as a crucial need to ensure better alignment of these systems with user needs.

Keamanan siber sebagai fondasi pengembangan aplikasi keuangan mobile: Studi literatur mengenai cybercrime dan mitigasinya

This study examines the importance of cybersecurity in mobile financial applications, particularly in the face of rising cybercrime threats. The aim of this research is to provide deeper insights into the significance of cybersecurity in mobile financial apps and to develop approaches that financial companies can adopt to protect their users' data and information. The research employs a qualitative descriptive method using a systematic literature review (SLR) approach. Meta-synthesis analysis is applied to synthesize data from 26 journal articles published between 2019 and 2024. The findings reveal that cybercrime in the financial and FinTech industries is driven by internal factors such as weak security systems, lack of system updates, limited human resource competencies, and careless user behavior. External factors include malware attacks, unclear regulations, and the use of advanced technology. To counter these, it is necessary to implement technologies such as firewalls and blockchain, strong risk management, enhanced network infrastructure, the establishment of cybersecurity teams, the development of clear regulations, and user education to raise awareness and improve cybersecurity. Therefore, the implementation of a comprehensive cybersecurity strategy is crucial in maintaining the integrity and trust of users in mobile financial applications.

Evaluating the influence of characteristics on the acceptance of artificial intelligence application: An empirical study

Artificial intelligence has accelerated the application scenarios and business value of map applications. However, the sustained intention of map applications utilizing artificial intelligence technology has not received sufficient attention and scholarly investigation. Therefore, this study constructs a theoretical model of the continuance intention of map applications, based on their characteristics and combined with user satisfaction and trust, aiming to explore the influence mechanism of this continuance intention. The results of structural equation modeling show that not only user trust and satisfaction have a positive effect on continuance intention towards map applications, but also interactivity characteristics, reliability characteristics, richness characteristics, and personalized characteristics have a positive effect on user satisfaction and trust towards map applications. This study reveals the influence mechanism of map application continuance intention, which can effectively guide map application operators to optimize their services and then provide practical insights for users to use map applications consistently.

THREAT MODEL FOR PAYMENT SYSTEMS

Payment systems are the backbone of modern economies, and their security is a critical aspect of maintaining user trust and safeguarding financial data. In this context, the development of threat models plays a central role in ensuring the protection of these systems from attacks. One of the most effective methodologies for threat modeling is STRIDE, which helps to structure and systematize the analysis of risks and threats. By utilizing the STRIDE model and incorporating a customized threat model for payment systems, the security of authentication processes and transaction handling can be significantly enhanced.

Ethical Artificial Intelligence Framework Theory (EAIFT): A New Paradigm for Embedding Ethical Reasoning in AI Systems

The rapid development of artificial intelligence (AI) has created several ethical issues, including bias, a lack of transparency, and privacy concerns, demanding the incorporation of ethical governance directly into AI systems. This study introduces the Ethical Artificial Intelligence Framework Theory (EAIFT), a novel approach to incorporating ethical reasoning into AI. It emphasizes real-time oversight, open decision-making, bias detection, and the ability to change ethical and legal norms. EAIFT advocates for establishing "ethical AI watchdogs" that automatically monitor and ensure the ethical operation of AI systems, together with dynamic compliance algorithms that can adapt to regulatory changes. The paradigm also encourages transparency and explainability to build user trust and detect and correct biases to ensure fairness. This paper employs a qualitative methodology that combines stakeholder interviews, content analysis, and expert commentary to evaluate EAIFT's potential to increase ethical accountability in various areas, including healthcare, banking, and criminal justice. The findings suggest that EAIFT outperforms existing ethical frameworks by proactively reducing biases, increasing transparency, and ensuring adherence to ethical standards. While presenting a comprehensive and adaptable technique, the study also acknowledges limitations in empirical testing and the need for additional research to widen EAIFT's applicability to future ethical challenges in artificial intelligence. The paper suggests future research subjects, such as empirical testing in different scenarios, a more in-depth examination of ethical risks, and the inclusion of the framework into new AI technologies to promote responsible AI governance by societal norms and values.

User Trust and Acceptance of Blockchain-based Web 4.0 Solutions

User Trust and Acceptance of Blockchain-based Web 4.0 Solutions

MOBILE BANKING ADOPTION: THE ROLE OF PERFORMANCE AND TRUST

This study examines the impact of performance and trust on the acceptance of mobile banking services in Indonesia. This study employs a survey questionnaire administered to 183 individuals in Indonesia who have adopted mobile banking services. The methodology employed is Partial Least Squares Structural Equation Modelling (PLS-SEM) to assess the correlation between latent and measured variables. The research findings indicate that performance has a notable and beneficial influence on trust. Additionally, trust plays a mediating role in the connection between performance and the adoption of mobile banking. These findings highlight the significance of achieving high performance to enhance user trust and promote the acceptance of mobile banking services. Therefore, banks and financial institutions must prioritize enhancing technical efficiency and establishing and upholding consumer confidence.

Exploring the effects of location information on perceptions of news credibility and sharing intention

In recent years, the integration of location-based services into social media platforms has seen a significant surge, coinciding with the growing challenges posed by the proliferation of fake news online. However, the influence of location data on readers’ perceptions of online news credibility, particularly in relation to the reporters’ whereabouts, remains unclear. To investigate this relationship, we conducted a 3 (Topics: crime, science, health) × 2 (Location anchor: event-anchored or participant-anchored) × 4 (Proximity to location anchor - no, same, close-by or faraway location) mixed-method online study (N = 288) on Prolific. Our data collection involved presenting participants with news articles and assessing their credibility assessments and sharing intentions based on the proximity of those disseminating the news to both the subject matter of the news and the audience consuming it. Our findings reveal that the proximity of the reporter’s location to the readers’ location had a noticeable adverse impact on perceptions of news credibility and the likelihood of sharing it. Furthermore, we also identified a weak positive correlation between sharing intentions and trust in social media platforms. In addition, we observed that crime news were generally perceived as less credible compared to health and science news. Our research contributes significantly to a nuanced understanding of how location-based cues impact user behaviour when interacting with online news articles. Furthermore, it provides design insights for social media platforms aiming to enhance user trust and promote pro-social behaviours.

Beyond the code: The impact of AI algorithm transparency signaling on user trust and relational satisfaction

This study investigates the effectiveness of AI-algorithm transparency signaling as a strategy to enhance organization-public relationships (OPRs) in AI-assisted communications. Building upon signaling theory and trust transfer theory, the study examines whether the AI algorithm transparency influences trust in AI systems by users and this trust can be transferred into trust in AI systems’ parent company, which in turn, influences the relational satisfaction with the company. An online experiment with 537 participants demonstrated that transparency signaling significantly improves users’ relational satisfaction with the AI parent company. However, this effect is mediated by trust in both the AI system and the parent company, rather than a direct relationship. Our findings offer practical guidelines for AI domain experts and public relations practitioners to deliberately convey the true essence of transparency in AI-mediated communication and ensure accountability in AI adoption, thereby improving public relations outcomes.