Search Results (389)

High cybersecurity risks and attacks cause information theft, unauthorized access to data and information, reputational damage, and financial loss in small and medium enterprises (SMEs). This creates a need to adopt information security systems of SMEs through innovation and compliance with information security policies. This study seeks to develop an integrated research model assessing the adoption of InfoSec systems in SMEs based on three existing theories, namely the technology acceptance model (TAM), theory of reasoned action (TRA), and unified theory of acceptance and use of technology (UTAUT). A thorough review of literature identified prior experience, enjoyment of new InfoSec technology, top management support, IT infrastructure, security training, legal-governmental regulations, and attitude as potential determinants of adoption of InfoSec systems. A self-developed and self-administered questionnaire was distributed to 418 employees, mid-level managers, and top-level managers working in SMEs operating in Riyadh, Saudi Arabia. The study found that prior experience, top management support, IT infrastructure, security training, and legal-governmental regulations have a positive impact on attitude toward InfoSec systems, which in turn positively influences the adoption of InfoSec systems. Gender, education, and occupation significantly moderated the impact of some determinants on attitude and, consequently, adoption of InfoSec systems. Such an integrated framework offers actionable insights and recommendations, including enhancing information security awareness and compliance with information security policies, as well as increasing profitability within SMEs. The study findings make considerable theoretical contributions to the development of knowledge and deliver practical contributions towards the status of SMEs in Saudi Arabia. Full article

Three-dimensional printed food has rapidly positioned itself at the intersection of food technology and personalized nutrition, opening up new perspectives for sustainable production, creative customization, and more efficient resource use. Although global interest in this innovation continues to grow, consumer acceptance remains largely underexplored in Central and Eastern Europe. This study analyzes how Romanian consumers approach the adoption of 3D-printed food by applying an extended UTAUT2 framework to a sample of 608 urban respondents. Using structural equation modeling, it examines the influence of expected effort, performance expectancy, social influence, and perceived compatibility on adoption intention, while price perception is introduced as a key mediating variable—a novel and meaningful contribution to the literature on food technology acceptance. Given the non-probabilistic sampling design, the difficulties encountered in measuring Hedonic Motivation and Facilitating Conditions, and the early diffusion stage of 3D food printing in Romania, the present work should be viewed as a robust exploratory investigation based on Structural Equation Modeling (SEM) among urban Romanian consumers, providing first empirical evidence on 3D-printed food acceptance in Eastern Europe rather than definitive conclusions for the entire population. The results highlight that utilitarian and social factors are decisive: expected effort enhances perceived performance, while performance, social influence, and compatibility significantly strengthen perceptions of price fairness. In turn, price perception strongly predicts consumers’ behavioral intention to adopt 3D-printed food. Hedonic motivation and facilitating conditions were not statistically significant and were therefore removed from the final model. These findings show that, in emerging food markets, consumers tend to make adoption decisions based more on rational value assessments than on novelty or convenience. The study contributes to theory by embedding price perception into the UTAUT2 framework and to practice by identifying the key elements that can boost market readiness—transparent pricing and closer alignment with consumer values. By filling an important gap in the empirical literature from Eastern Europe and focusing on price as a cognitive bridge between technological and psychological drivers, this paper offers a timely and relevant contribution to ongoing research on consumer perception and acceptance of food innovations. For Eastern European food innovation research, this study provides one of the first quantitative analyses of 3D-printed food acceptance that explicitly links technology-related beliefs to price perception in a regional, price-sensitive context. Full article

The rapid advancement of artificial intelligence (AI) technologies has created both opportunities and challenges in higher education. AI tools are increasingly regarded as potentially transformative, yet also contested, resources for enhancing teaching and learning. This study investigates the behavioral intentions and use behavior of Generative AI (GenAI) tools among Portuguese university students by applying the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model. Seven predictors (performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and price/value) were included to develop a model explaining the behavioral intention and use behavior of AI tools. Data were collected from 323 Portuguese university students and analyzed using partial least squares structural equation modeling. The results show that only hedonic motivation and habit significantly influence behavioral intention, with habit emerging as the strongest predictor. Behavioral intention, in turn, positively affects use behavior. Moreover, gender also moderates the relationship between habit and behavioral intention. These findings deepen our understanding of GenAI tools’ acceptance in higher education and underscore the central roles of enjoyment and routine in adoption processes. They further provide insights to support institutional strategies, inform curriculum design, and support the ethical integration of GenAI technologies in academic contexts. Full article

County-wide distributed rooftop photovoltaic (DRPV) systems, as an emerging form of renewable energy development, constitute a critical component for the low-carbon energy transition and carbon reduction. However, the pilot implementation in China has faced many challenges, with resistance from rural residents being a key issue requiring urgent resolution. This study aimed to investigate the underlying factors influencing their participation in DRPV and identify the key determinants. The topic modeling and evolutionary analysis were first conducted based on the multi-platform online textual data. The theoretical model was constructed combining the antecedent variables identified by the online textual analysis and the classic Unified Theory of Acceptance and Use of Technology (UTAUT) framework. This model was validated through questionnaire surveys and structural equation modeling. The results revealed that facilitating conditions were the core determinant of rural residents’ participation in DRPV systems. Government-led safeguard mechanisms served as the primary enhancer of perceived convenience. Additionally, effort expectancy (0.301), performance expectancy (0.253), and social influence (0.424) all positively correlated with participation intention, with social influence exhibiting the strongest impact. Notably, rural residents equally prioritize environmental benefits and economic returns from DRPV systems. These findings provided policy insights for promoting DRPV projects in the future. Full article

This study examines the effects of trust in AI, willingness to accept AI, and perceptions of AI performance on brand loyalty in the hospitality industry. The study further posits that purchase intentions mediate the relationship between understudied independent variables and brand loyalty. Drawing on the Technology Acceptance Model, Unified Theory of Acceptance Use of Technology UTAUT, and Trust–Satisfaction theory as the theoretical foundation, the study posits seven hypotheses. Data were collected via an online platform; ultimately, 183 participants participated in the survey. The study tests the hypotheses using Hayes’s Macro PROCESS Model 4 mediation, where the direct and indirect effects were established. The study observes that the direct and indirect effects of trust in AI and the perceived performance of AI on brand loyalty were significant. However, only the indirect effect was significant for the willingness to accept AI on brand loyalty. These observations show that the use of AI-enabled services enhances a stronger intention to book, leading to an improved brand loyalty. The results extend the body of knowledge in this area by linking the unified framework of trust in AI, AI perceived performance, and willingness to accept AI to brand loyalty in the hospitality industry. For managers and hoteliers, this finding is clear, emphasising that the transparent and reliable use of AI enhances purchase intentions and improves brand loyalty. Full article

This study examines the readiness of the accounting and auditing profession to adopt disruptive innovations, with a particular focus on sustainable digital transformation. It investigates the factors influencing auditors’ and accountants’ intention to adopt blockchain technology (BT) as a sustainable digital infrastructure that enhances transparency, accountability, traceability, and operational efficiency. The research integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with the third iteration of the Technology Acceptance Model (TAM3), providing a comprehensive framework for understanding the sustainable adoption of emerging technologies. A quantitative research design was employed through an online questionnaire, collecting empirical data from 394 prospective and existing blockchain users within the accounting and auditing professions in Saudi Arabia. Data validation and hypothesis testing were conducted using Structural Equation Modeling (SEM) with Smart-PLS software (version 4.1.0.8). The results reveal a strong and significant positive influence of performance expectancy (PE), effort expectancy (EE), and social influence (SI) on intention to use (IU). Additionally, PE is positively and significantly associated with job relevance (JR) and output quality (OQ). Conversely, computer self-efficacy (CSE) shows no significant impact on EE, while compatibility (CO) positively influences EE but not IU. Moreover, EE has a substantial effect on PE. These findings contribute to the growing discourse on how disruptive ICTs are reshaping the accounting and auditing profession while supporting sustainable digital transformation. The study provides practical insights for policymakers, regulators, corporate leaders, and blockchain providers seeking to leverage BT not only for technological efficiency but also to advance long-term organizational sustainability and responsible governance. Full article

Due to the limitations of general-purpose generative artificial intelligence (GenAI) platforms in meeting the needs of fashion design, AI-based integrated fashion design platforms (AIIFDP) have emerged as a more suitable solution. As the next generation of designers, fashion design students play a pivotal role in shaping the optimization and promotion of AIIFDP. However, research on their continuance intention toward such platforms remains limited. This study constructs an integrated model by combining the Unified Theory of Acceptance and Use of Technology (UTAUT) with the Expectation-Confirmation Model (ECM), and extending it with variables such as personal innovativeness, habit, and perceived intelligence. Using a multi-stage SEM-ANN analysis, the study empirically analyzed data from 486 questionnaires completed by fashion design students in China. The results suggest that satisfaction is the most significant positive factor influencing continuance intention. Moreover, performance expectancy, social influence, perceived intelligence, and habit also exert significant effects. This study broadens the segmented perspective on the application of GenAI in design education and validates the applicability of the extended UTAUT-ECM model in the context of AIIFDP. It also provides theoretical foundations and multi-level strategic recommendations for optimizing AIIFDP products and guiding their integration into educational practices. Full article

This study aims to examine the factors influencing the acceptance of extruded pellet (EP) usage among Korea’s olive flounder farming households by analyzing their acceptance factors to provide recommendations for its wider adoption. A survey was conducted among olive flounder farming households, and 188 valid questionnaires were collected. To examine the factors influencing EP acceptance intention, the UTAUT2 (extended unified theory of acceptance and use of technology) model was used. The independent variables were categorized into performance expectancy, effort expectancy, facilitating conditions, social influence, price value, and reliability as independent variables, while acceptance intention was considered as the dependent variable, to derive measurement items. In addition, the differences between the two groups were analyzed by using the aquaculture region and the manager’s experience as moderating variables. The hypothesis testing showed that performance expectancy, effort expectancy, social influence, price value, and reliability factors had a positive effect on acceptance intention, while facilitating conditions did not show a significant effect. The analysis of the moderating effect of the aquaculture region indicated a significant difference between the Jeju-do and Jeollanam-do groups. Conversely, the moderating effect of experience showed no significant difference between those with more experience (≥10 years) and those with less (<10 years). Full article

This study used Q methodology to explore and categorise caregivers’ subjective perceptions of artificial intelligence (AI)-powered ‘virtual human’ (AVH) devices in caring for older adults. We derived 123 initial statements from literature and focus groups and narrowed them to 34 statements as the final Q sample. Seventeen caregivers, nurses, and social workers completed the Q-sorting procedure. Using principal component analysis and Varimax rotation in Ken-Q, we identified three perception types: Active Acceptors, who emphasise the devices’ practical utility in patient communication; Improvement Seekers, who conditionally accept the technology while seeking greater accuracy and effectiveness; and Emotional Support Seekers, who view the device as a tool for emotional relief and psychological support. These findings suggest that technology acceptance in caregiving extends beyond functional utility. It also involves trust, affective experience, and interpersonal interaction. This study integrates multiple frameworks, including the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), Science and Technology Studies (STS), and Human–Machine Communication (HMC) theory, to provide a multifaceted understanding of caregivers’ acceptance of AI technology. The results offer valuable implications for designing user-centred AI care devices and enhanced emotional and communicative functions. Full article

Robotaxi services represent a major step in the commercialization of autonomous driving, offering efficiency, consistency, and safety benefits. However, despite technological advances, their large-scale adoption is far from guaranteed. Most urban users already rely on mature ride-hailing platforms such as Didi and Uber, making the real behavioral question not whether to adopt Robotaxi, but whether to migrate from existing services. Prior studies based on TAM, UTAUT, or trust models have primarily examined users’ initial adoption decisions, overlooking the substitution behavior that better captures how people shift between competing mobility services in real contexts. This study addresses this gap by applying the Push–Pull–Mooring (PPM) framework to examine users’ migration from ride-hailing to Robotaxi services, based on survey data collected from 1206 respondents across four Chinese cities (Beijing, Shanghai, Guangzhou, and Wuhan). The model was tested using structural equation modeling and multi-group analysis (SEM–MGA). Push factors reflect negative experiences with ride-hailing, including social anxiety and insecurity caused by drivers’ behaviors; pull factors emphasize Robotaxi’ autonomy and service reliability; while mooring factors capture habitual ride-hailing use and perceived Robotaxi risk. Findings indicate that push and pull factors significantly promote migration intentions, whereas mooring factors hinder them. Among all factors, perceived risk exerted the strongest negative effect (β = −0.36), underscoring its critical role as a barrier to Robotaxi migration. Gender differences are also evident, with women more sensitive to risks and men more influenced by reliability. By situating adoption within a migration context, this study enriches high-risk innovation theory and offers practical guidance for designing gender-sensitive and user-specific promotion strategies. Full article

This study empirically examines the impact of artificial intelligence (AI) service competency on digital economic activities, digital social participation, and daily life satisfaction. It also investigates the mediating role of e-commerce utilization in these relationships. The ultimate aim is to provide practical implications for enhancing citizens’ quality of life and promote their active participation in the digital society. This study employed data from 7001 respondents drawn from the 2023 Digital Information Gap Survey. The analysis was conducted using SPSS 29.0 and structural equation modeling (SEM) to examine the structural relationships between variables. The results revealed that perceived usefulness of AI technology had a significant positive effect on digital economic activities, while digital information competency positively influenced both digital economic activities and daily life satisfaction. In contrast, attitude toward AI technology did not have a significant effect on digital economic activities and even showed a negative association with digital social participation. Furthermore, digital information competency demonstrated a significant indirect effect on the overall digital outcomes through the mediating role of the level of e-commerce utilization. This study applies an integrated theoretical framework combining the Theory of Reasoned Action (TRA) and the Unified Theory of Acceptance and Use of Technology (UTAUT). It goes beyond theoretical interpretation by delivering practical implications that offer academic and policy value, particularly for developing policy strategies such as reducing the digital divide. Full article

This paper reports a study conducting an in-depth analysis of the impacts of ethical training on the adoption of AI tools among computer science students in higher vocational colleges. These students will serve as the pivotal human factor for advancing the field of AI. Aiming to explore practical models for integrating AI ethics into computer science education, the research seeks to promote more responsible and effective AI application and therefore become a positive influence in the field. Employing a mixed-methods approach, the study included 105 students aged 20–24 from a vocational college in Guangdong Province, a developed region in China. Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, a five-point Likert scale was used to evaluate the participants’ perceptions of AI tool usage based on ethical principles. The Structural Equation Modeling (SEM) results indicate that while participants are motivated to adopt AI technologies in certain aspects, performance expectancy negatively impacts their intention and actual usage. After systematically studying and understanding AI ethics, participants attribute a high proportion of responsibility (84.89%) to objective factors and prioritized safety (27.11%) among eight ethical principles. Statistical analysis shows that habit (β = 0.478, p < 0.001) and hedonic motivation (β = 0.239, p = 0.004) significantly influence behavioral intention. Additionally, social influence (β = 0.234, p = 0.008) affects use behavior. Findings regarding factors that influence AI usage can inform a strategic framework for the integration of ethical instruction in AI applications. These findings have significant implications for curriculum design, policy formulation, and the establishment of ethical guidelines for AI deployment in higher educational contexts. Full article

Background: Mental health is increasingly prioritized in Saudi Arabia, with growing interest in digital solutions. Objectives: The study’s objective was to assess awareness, acceptance, and use of mobile mental health applications among Saudis. Methods: A cross-sectional online survey, based on the UTAUT model, explored performance expectancy, effort expectancy, social influence, and privacy concerns among 1613 participants. Results: While 68.9% were aware of at least one mental health app, only 20% actively used them. Awareness was influenced by gender, age, employment, marital status, and region, whereas utilization depended on gender, age, education, region, and acceptance. Performance expectancy strongly predicted usage. Conclusions: Despite high awareness, usage of mobile mental health applications remains low in Saudi Arabia. Demographic factors affect awareness, and acceptance drives utilization. App developers should consider these factors to enhance engagement and effectiveness. Full article

The rapid digital transformation of higher education creates opportunities and challenges, particularly in professional programmes where students must balance academic learning with preparation for operational duties, such as in medicine, engineering, and defence. While digital technologies are widely used in higher education, their sustainable integration into professional contexts, especially security and defence education, remains underexplored. This study investigates the determinants of perceived e-learning usefulness among undergraduates (cadets) at the Lithuanian Military Academy, applying an adapted technology acceptance model framework. A structured questionnaire was used to measure constructs related to distance learning effectiveness, classroom innovation, security, sustainability of digital systems, and individual learning preferences, with hypotheses tested through mediation and moderated mediation models. The results indicate that the effectiveness of distance learning is the strongest factor influencing intention to use it, supported by the roles of classroom innovation and system security. Perceived usefulness further emerges as both a direct predictor of adoption and a conditional factor shaping the impact of pedagogical and infrastructural design on acceptance. These findings extend traditional technology acceptance frameworks and provide new insights into how sustainable digital teaching can be fostered in higher professional education, where academic quality and operational readiness must be aligned. Full article

The development of generative AI has disrupted various fields, and the field of online healthcare is no exception. However, there is a lack of research on patients’ intention to adopt generative AI on online healthcare platforms. Therefore, the aim of this study is to investigate the factors influencing patients’ intention to adopt generative AI. Employing a questionnaire-based survey, we explore the factors influencing patients’ intention to adopt generative AI through the UTAUT2 model, considering the moderating effects of construal level, health literacy, and AI literacy. We find that performance expectancy, social influence, and facilitating conditions are positively associated with patients’ intention. Surprisingly, effort expectancy and hedonic motivation do not have a significant impact on patients’ intention. Construal level positively moderates the relationship between performance expectancy and patients’ intention; health literacy negatively moderates the relationship between social influence and patients’ intention. AI literacy positively moderates the relationship between effort expectancy and patients’ intention but negatively moderates the relationship between social influence and patients’ intention. This study enriches UTAUT2 theory and provides practical insights for the development and promotion of generative AI on online healthcare platforms. Full article