Search Results (109)

The growing need for secure and privacy-preserving identity management in industrial environments has exposed the limitations of traditional, centralized authentication systems. In this context, SIBERIA was developed as a modular solution that empowers users to control their own digital identities, while ensuring robust protection of critical services. The system is designed in alignment with European standards and regulations, including EBSI, eIDAS 2.0, and the GDPR. SIBERIA integrates a Self-Sovereign Identity (SSI) framework with a decentralized blockchain-based infrastructure for the issuance and verification of Verifiable Credentials (VCs). It incorporates multi-factor authentication by combining a voice biometric module, enhanced with spoofing-aware techniques to detect synthetic or replayed audio, and a behavioral biometrics module that provides continuous authentication by monitoring user interaction patterns. The system enables secure and user-centric identity management in industrial contexts, ensuring high resistance to impersonation and credential theft while maintaining regulatory compliance. SIBERIA demonstrates that it is possible to achieve both strong security and user autonomy in digital identity systems by leveraging decentralized technologies and advanced biometric verification methods. Full article

The rapid proliferation of electronic waste, driven by the short lifecycle of smartphones and planned obsolescence strategies, presents escalating global environmental challenges. To address these issues from a systems perspective, this study develops an agent-based modeling (ABM) framework that simulates consumer decisions and stakeholder interactions within the smartphone reuse and recycling ecosystem. The model incorporates key behavioral drivers—privacy concerns, moral norms, and financial incentives—to examine how social and economic factors shape consumer behavior. Four primary agent types—consumers, manufacturers, recyclers, and second-hand retailers—are modeled to capture complex feedback and market dynamics. Calibrated using empirical data from Jiangsu Province, China, the simulation reveals a dominant consumer tendency to store obsolete smartphones rather than engage in reuse or formal recycling. However, the introduction of government subsidies significantly shifts behavior, doubling participation in second-hand markets and markedly improving recycling rates. These results highlight the value of integrating behavioral insights into environmental modeling to inform circular economy strategies. By offering a flexible and behaviorally grounded simulation tool, this study supports the design of more effective policies for promoting responsible smartphone disposal and lifecycle extension. Full article

Design and use of wearable technology have grown exponentially, particularly in consumer products and service sectors, e.g., healthcare. However, there is a lack of a comprehensive understanding of wearable technology in consumer acceptance. This systematic review utilized a PRISMA on peer-reviewed articles published between 2014 and 2024 and collected on WoS, Scopus, and ScienceDirect. A total of 38 full-text articles were systematically reviewed and analyzed using bibliometric, thematic, and descriptive analysis to understand the technical functions of digital wearable products (DWPs) in consumer acceptance. The findings revealed four key functions: (i) wearable technology, (ii) appearance and design, (iii) biomimetic innovation, and (iv) security and privacy, found in eight types of DWPs, among them smartwatches, medical robotics, fitness devices, and wearable fashions, significantly predicted the customers’ acceptance moderated by the behavioral factors. The review also identified five key outcomes: health and fitness, enjoyment, social value, biomimicry, and market growth. The review proposed a comprehensive acceptance model that combines biomimetic principles and AI-driven features into the technical functions of the technical function model (TAM) while addressing security and privacy concerns. This approach contributes to the extended definition of TAM in wearable technology, offering new pathways for biomimetic research in smart devices and robotics. Full article

Generative AI (GenAI) tools, including ChatGPT, Microsoft Copilot, and Google Gemini, are rapidly reshaping higher education by transforming how students, educators, and researchers engage with learning, teaching, and academic work. Despite their growing presence, the adoption of GenAI remains inconsistent, largely due to the absence of universal guidelines and trust-related concerns. This study examines how trust, defined across three key dimensions (accuracy and relevance, privacy protection, and nonmaliciousness), influences the adoption and use of GenAI tools in academic environments. Using survey data from 823 participants across different academic roles, this study employs multiple regression analysis to explore the relationship between trust, user characteristics, and behavioral intention. The results reveal that trust is primarily experience-driven. Frequency of use, duration of use, and self-assessed proficiency significantly predict trust, whereas demographic factors, such as gender and academic role, have no significant influence. Furthermore, trust emerges as a strong predictor of behavioral intention to adopt GenAI tools. These findings reinforce trust calibration theory and extend the UTAUT2 framework to the context of GenAI in education. This study highlights that fostering appropriate trust through transparent policies, privacy safeguards, and practical training is critical for enabling responsible, ethical, and effective integration of GenAI into higher education. Full article

As data becomes a core driver of modern business innovation, mobile applications increasingly collect and process users’ personal information, posing significant challenges to the effectiveness of informed consent and the legitimacy of user authorization. Existing research on privacy informed consent mechanisms has predominantly focused on privacy policy texts and normative legal discussions, often overlooking a critical touchpoint—the launch-time privacy pop-up window. Moreover, empirical investigations from the user’s perspective remain limited. To address these issues, this study employs a two-stage approach combining compliance audit and grounded theory. The preliminary audit of 21 mobile apps assesses the compliance of privacy pop-ups, and the formal study uses thematic analysis of interviews with 19 participants to construct a dual-path explanatory framework. Key findings reveal that: (1) while the reviewed apps partially safeguarded users’ right to be informed, compliance deficiencies still persist; (2) trust and privacy fatigue emerge as dual motivations driving user consent. Trust plays a critical role in amplifying the impact of positive messages within privacy pop-ups by enhancing the consistency among users’ cognition, affect, and behavior, thereby reducing resistance to privacy consent and improving the effectiveness of the current informed consent framework. Conversely, privacy fatigue increases the inconsistency among these factors, undermining consent effectiveness and exacerbating the challenges associated with informed consent. This study offers a user-centered framework to explain the dynamics of informed consent in mobile privacy pop-ups and provides actionable insights for regulators, developers, and privacy advocates seeking to enhance transparency and user autonomy. Full article

Generative artificial intelligence (GAI) is emerging as a disruptive force, both economically and socially, with its use spanning from the provision of goods and services to everyday activities such as healthcare and household management. This study analyzes the enabling and inhibiting factors of GAI use in Spain based on a large-scale survey conducted by the Spanish Center for Sociological Research on the use and perception of artificial intelligence. The proposed model is based on the Theory of Planned Behavior and is fitted using machine learning techniques, specifically decision trees, Random Forest extensions, and extreme gradient boosting. While decision trees allow for detailed visualization of how variables interact to explain usage, Random Forest provides an excellent model fit (R² close to 95%) and predictive performance. The use of Shapley Additive Explanations reveals that knowledge about artificial intelligence, followed by innovation orientation, is the main explanatory variable of GAI use. Among sociodemographic variables, Generation X and Z stood out as the most relevant. It is also noteworthy that the perceived privacy risk does not show a clear inhibitory influence on usage. Factors representing the positive consequences of GAI, such as performance expectancy and social utility, exert a stronger influence than the negative impact of hindering factors such as perceived privacy or social risks. Full article

Immersive technologies (IMTs) have significantly impacted the tourism sector by offering experiences that enhance engagement with destinations. Although previous research confirms that IMT affects travelers’ behavioral intentions, there is a lack of studies specifically focusing on the post-experience context of agritourism and the factors contributing to technological resistance. This study introduces a conceptual model that combines the Diffusion of Innovation framework, the technology acceptance model, and the psychological factors related to innovation resistance to examine the decision-making processes of IMT users in the post-experience context of agritourism. The research model is evaluated through partial least squares structural equation modeling (PLS-SEM) techniques involving 400 users who engaged with IMT for a duration not exceeding 3 months. The findings indicate that the amount of storytelling content, which enhances engagement in agritourism, significantly affects users’ perceptions of IMT and their intentions to revisit and continue using IMT. Additionally, factors related to compatibility, along with privacy and security risks, influence the reluctance or readiness to adopt IMT and the decision to revisit a destination. These findings contribute to the understanding necessary to develop content and apply IMT in the agritourism sector to promote long-term sustainability. Full article

Insider threats pose significant challenges to organizations, seriously endangering information security and privacy protection. These threats arise when employees with legitimate access to systems and databases misuse their privileges. Such individuals may alter, delete, or insert data into datasets, sell customer or client email addresses, leak strategic company plans, or transfer industrial and intellectual property information. These actions can severely damage a company’s reputation, result in revenue losses and loss of competitive advantage, and, in extreme cases, lead to bankruptcy. This study presents a novel solution that examines how organizational factors such as job satisfaction and security, organizational support, attachment, commitment, involvement in information security, and organizational norms influence employees’ attitudes and intentions, thereby mitigating insider threats. A key strength of this research is its integration of two foundational theories: the Social Bond Theory (SBT) and the Theory of Planned Behavior (TPB). The results reveal that job satisfaction and security, affective and normative commitment, information security training, and personal norms all contribute to reducing insider threats. Furthermore, the findings indicate that employees’ attitudes, perceived behavioral control, and subjective norms significantly influence their intentions to mitigate insider threats. However, organizational support and continuance commitment were not found to have a significant impact. Full article

The rapid evolution of the transport industry requires a deep understanding of user preferences for emerging mobility solutions, particularly carsharing (CS) and autonomous vehicles (AVs). This study employs machine learning techniques to model transport mode choice, with a focus on traffic safety perceptions of people towards CS and privately shared autonomous vehicles (PSAVs). A stated preference (SP) survey is conducted to collect data on travel behavior, incorporating key attributes such as trip time, trip cost, waiting and walking time, privacy, cybersecurity, and surveillance concerns. Sociodemographic factors, such as income, gender, education, employment status, and trip purpose, are also examined. Three gradient boosting models—CatBoost, XGBoost, and LightGBM are applied to classify user choices. The performance of models is evaluated using accuracy, precision, and F1-score. The XGBoost demonstrates the highest accuracy (77.174%) and effectively captures the complexity of mode choice behavior. The results indicate that CS users are easily classified, while PSAV users present greater classification challenges due to variations in safety perceptions and technological acceptance. From a traffic safety perspective, the results emphasize that companionship, comfort, privacy, cybersecurity, safety in using CS and PSAVs, and surveillance significantly influence CS and PSAV acceptance, which leads to the importance of trust in adopting AVs. The findings suggest that ensuring public trust occurs through robust safety regulations and transparent data security policies. Furthermore, the envisaged benefits of shared autonomous mobility are alleviating congestion and promoting sustainability. Full article

As smart voice assistants (SVAs) become increasingly integrated into digital commerce, understanding the psychological factors driving their adoption or resistance is essential. While prior research has addressed the impact of privacy concerns, few studies have explored the competing forces that shape user decisions. This study investigates the dual role of privacy cynicism as a context-specific belief influencing both trust (reason-for) and perceived creepiness (reason-against)—which in turn affect attitudes, behavioral intentions, and resistance toward SVA usage, based on the Behavioral Reasoning Theory (BRT). The study used a convenience sampling method, gathering data from 250 Turkish consumers aged 18–35 through an online survey technique. The research model was analyzed using PLS-SEM. The findings revealed that perceived creepiness increases resistance intention but does not significantly affect attitudes toward using SVAs. Perceived cynicism was found to positively influence perceived trust, and perceived trust, in turn, increased both behavioral intentions and attitudes toward using SVAs. Furthermore, attitudes toward SVA usage decreased resistance intention but increased behavioral intention. The results emphasize consumer trust and skepticism in AI-driven marketing. The study offers both theoretical contributions by extending BRT with a novel dual-path conceptualization of privacy cynicism, and practical implications for developers aiming to boost SVA adoption through trust-building and privacy assurance strategies. Full article

The rapid development of autonomous driving technology has been a key driver for the emergence of driverless delivery vehicles. To promote wider adoption, it is essential to address consumers’ concerns about safety and reliability, leverage psychological factors, and implement supportive policies that encourage technology adoption while ensuring public safety and privacy. Therefore, it is necessary to explain and predict consumers’ behavior and intention to adopt driverless delivery vehicles. To this end, this study extends the Technology Acceptance Model (TAM) to include technological complexity and perceived trust. This study evaluates the model by applying necessary condition analysis (NCA) and partial least squares structural equation modeling (PLS-SEM) to analyze data from 579 respondents from Jiangsu Province, China. This study explores the sustainability implications of autonomous delivery vehicles, highlighting their potential to reduce environmental impact and promote a more sustainable transportation system. The outcomes indicate that perceived ease of use (PEU), attitude, perceived trust, technological complexity (TECOM), and perceived usefulness (PU) are significant determinants and necessary conditions of consumers’ intention to adopt driverless delivery vehicles. Perceived trust and TECOM had a significant and indirect influence on consumers’ intention to adopt driverless delivery vehicles via PU and PEU. Perceived trust and technological complexity had a substantial impact on consumers’ adoption intention of driverless delivery vehicles. The study recommends that managers work closely with regulators to ensure their technologies meet all local standards and regulations. It also recommends its potential to reduce carbon emissions, improve energy efficiency, and contribute to a more sustainable transportation system. Full article

VR technology is increasingly being integrated into daily life, with virtual book communities emerging as novel platforms for immersive reading and interaction. This study investigates how internal and external factors jointly influence users’ usage intention from psychological and behavioral science perspectives. A multivariate structural equation model based on three-dimensional perception theory was developed and tested through a survey of individuals with prior VR reading experience. The model examines the roles of task–technology fit, privacy and security risks, emotional resonance, self-expression, and the sense of belonging. The results reveal that task–technology fit positively influences usage intention, while privacy and security risk has a negative effect. Internally, emotional resonance and a sense of belonging significantly enhance usage intention. Furthermore, emotional resonance mediates the relationship between self-expression and both sense of belonging and usage intention, while sense of belonging also mediates between emotional resonance and usage intention. These findings underscore the critical interplay between technical attributes and affective factors in shaping engagement with VR-based reading platforms. This study offers new insights into user acceptance mechanisms in virtual book communities, and provides a theoretical foundation and practical implications for enhancing user experience and adoption in digital library systems. Full article

With the widespread application of AI-generated content (AIGC) tools in creative domains, users have become increasingly concerned about the ethical issues they raise, which may influence their adoption decisions. To explore how ethical perceptions affect user behavior, this study constructs an ethical perception model based on the trust–risk theoretical framework, focusing on its impact on users’ adoption intention (ADI). Through a systematic literature review and expert interviews, eight core ethical dimensions were identified: Misinformation (MIS), Accountability (ACC), Algorithmic Bias (ALB), Creativity Ethics (CRE), Privacy (PRI), Job Displacement (JOD), Ethical Transparency (ETR), and Control over AI (CON). Based on 582 valid responses, structural equation modeling (SEM) was conducted to empirically test the proposed paths. The results show that six factors significantly and positively influence perceived risk (PR): JOD (β = 0.216), MIS (β = 0.161), ETR (β = 0.150), ACC (β = 0.137), CON (β = 0.136), and PRI (β = 0.131), while the effects of ALB and CRE were not significant. Regarding trust in AI (TR), six factors significantly negatively influence it: CRE (β = −0.195), PRI (β = −0.145), ETR (β = −0.148), CON (β = −0.133), ALB (β = −0.113), and ACC (β = −0.098), while MIS and JOD were not significant. In addition, PR has a significant negative effect on TR (β = −0.234), which further impacts ADI. Specifically, PR has a significant negative effect on ADI (β = −0.259), while TR has a significant positive effect (β = 0.187). This study not only expands the applicability of the trust–risk framework in the context of AIGC but also proposes an ethical perception model for user adoption research, offering empirical evidence and practical guidance for platform design, governance mechanisms, and trust-building strategies. Full article

Power consumption data play a crucial role in demand management and abnormality detection in smart grids. Despite its management benefits, analyzing power consumption data leads to profiling consumers and opens privacy issues. To demonstrate this, we present a power profiling model for smart grid consumers based on real-time load data acquired from smart meters. It profiles consumers’ power consumption behavior by applying the daily load factor and the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features can be extracted. By this model, two load types are defined and the related load patterns are extracted for classifying consumption behavior by DTW. The classification methodology is discussed in detail. To evaluate the performance of the proposed model for profiling, we analyze the time-series load data measured by a smart meter in a real case. The results demonstrate the effectiveness of the proposed profiling method, achieving an F-score of $0.8372$ for load type clustering in the best case and an overall accuracy of $77.17\%$ for power profiling. Full article

The rapid advancements in artificial intelligence (AI) have significantly transformed various domains, including education, by introducing innovative tools that reshape teaching and learning processes. This research investigates the perceptions and attitudes of students towards the use of AI tools in their academic activities, focusing on constructs such as perceived usefulness, the perceived ease of use, security and privacy concerns, and both positive and negative attitudes towards AI. On the basis of Technology Acceptance Model (TAM) and the General Attitudes towards Artificial Intelligence Scale (GAAIS), this research seeks to identify the factors influencing students’ behavioral intentions and actual adoption of AI tools in educational settings. A structured survey was administered to students at Multimedia University, Malaysia, capturing their experiences and opinions on widely used AI tools such as ChatGPT, Quillbot, Grammarly, and Perplexity. Hypothesis testing was used to evaluate the statistical significance of relationships between the constructs and behavioral intention and actual use of the AI tools. The findings reveal a high level of engagement with AI tools among University students, primarily driven by their perceived benefits in enhancing academic performance, improving efficiency, and facilitating personalized learning experiences. The findings also uncover significant concerns related to data security, privacy, and the potential over-reliance on AI tools, which may hinder the development of critical thinking and problem-solving skills. Full article