Search Results (2,320)

The sustainable integration of technology into educational practices is pivotal for modern teaching and learning. Grounded in the Technology Acceptance Model (TAM), this study explores the psychological and contextual factors that influence technology acceptance among pre-service and in-service teachers. Employing a nonexperimental, cross-sectional design, data were collected from 347 participants to examine the relationships between perceived usefulness, perceived ease of use, attitude toward use, behavioural intention, and actual system use. Results indicate that pre-service teachers demonstrate stronger openness to technology adoption, driven primarily by attitudinal factors, whereas in-service teachers’ acceptance is more closely linked to perceived utility and usability. This study advances the TAM by integrating a dual serial mediation model and testing the moderating role of professional status, thereby offering a nuanced understanding of sustainable digital engagement across career stages. Our findings underscore the importance of fostering positive perceptions and providing differentiated support throughout teachers’ professional trajectories to achieve long-term, meaningful technology adoption in education. Full article

Indonesia’s worsening air pollution and traffic emissions have thrust electric vehicles (EVs) into the spotlight, but what really drives Indonesians to make the switch? This study integrates Protection Motivation Theory with green branding and policy frameworks to explain electric vehicle (EV) adoption in Indonesia. Using a nationwide survey (n = 986) and partial-least-squares structural-equation modeling, we test how environmental awareness, consumer expectancy, threat appraisal, and coping appraisal shape adoption both directly and through green brand image (GBI), while perceived policy incentives moderate the GBI–adoption link. The model accounts for 54% of the variance in adoption intention. These findings highlight that combining public awareness campaigns, compelling green brand messaging, and carefully calibrated policy incentives is essential for accelerating Indonesia’s transition to cleaner transport. Full article

To enhance the performance of online food delivery (OFD) services, it is essential to strengthen consumers’ intentions to use OFD apps, which are the core interface of this business. Accordingly, this study aims to identify the cognitive mechanisms that shape consumers’ intentions to use OFD apps and explore strategies to encourage their adoption. To achieve this, the study develops a research model that incorporates segmented dimensions of service convenience as key motivational factors, along with variables from the Technology Acceptance Model (TAM). A survey was conducted among OFD consumers in China, and the proposed research model was empirically tested using data from 478 valid responses. The analysis revealed that all six dimensions of service convenience serve as significant motivational drivers of OFD app usage. Furthermore, the study demonstrates that consumers’ coupon proneness and user-generated online reviews have significant moderating effects that reinforce the mechanism by which consumers adopt and use OFD apps. The findings and implications discussed in this study are expected to provide valuable insights and practical guidance for formulating effective strategies to promote more active consumer engagement with OFD apps in the future. Full article

This study explores the determinants of startup intention within the context of e-commerce platform-based startups in South Korea. We employ an extended technology acceptance model (TAM) that integrates individual, social, and entrepreneurial characteristics. A two-step analytical approach is applied, combining variable extraction through data mining and hypothesis testing using structural equation modeling. The results indicate that personal and social factors—such as entrepreneurial mindset and social influence—positively affect perceived usefulness, while job relevance and exposure to successful startup models enhance perceived ease of use. In contrast, security concerns and technological barriers negatively impact these relationships, posing critical obstacles to platform-based startups. This study extends the TAM framework to the platform-based startup context, offering theoretical contributions and proposing policy implications, including promoting digital literacy, developing entrepreneurial networks, and addressing security and regulatory issues. These insights offer a deeper understanding of how platform environments shape entrepreneurial behavior, providing practical guidance for startup founders, developers, and policymakers. Full article

Objectives: To assess the role of a real-time therapeutic drug monitoring (TDM)-guided expert clinical pharmacological advice (ECPA) program of isavuconazole in preventing under- or overexposure with the intent of improving efficacy and safety outcomes in the critically ill patients. Methods: This retrospective study included critical patients receiving intravenous isavuconazole for prophylaxis or treatment of invasive fungal infections (IFI) and undergoing at least one TDM-guided ECPA in the period 1 March 2021–31 March 2025. Desired isavuconazole exposure was defined as trough concentrations (C_min) of 1.0–5.1 mg/L. Efficacy outcome was assessed by means of bronchoalveolar (BAL) galactomannan (GM) index, breakthrough IFI, and 30-day mortality rate, whereas safety was assessed by means of hepatic test disturbances (HTD). Univariate analysis was carried out for assessing potential variables associated with isavuconazole under- or overexposure and for comparing features of solid organ transplant (SOT) recipients vs. non-SOT patients. Proportions of isavuconazole C_min underexposure, desired exposure, and overexposure were assessed at different timepoints from starting therapy. Trends over time of HTD in relation to isavuconazole exposure were assessed separately in patients having HTD or not at baseline. Results: Overall, 32 critical patients were included. A total of 166 TDM-guided ECPAs were provided. Median (IQR) average isavuconazole C_min was 3.5 mg/L (2.1–4.6 mg/L). Proportions of ECPAs with isavuconazole C_min under- and overexposure were 4.2% (7/166) and 16.3% (27/166), respectively. Patients experiencing underexposure had higher body mass index (30.1 vs. 25.5 kg/m²; p < 0.001). Trends of isavuconazole C_min under- and overexposure changed over time, significantly decreasing the former (10.5% <7 days vs. 4.3% 7–28 days vs. 0.0% >28 days; p < 0.001) and increasing the latter (5.3% <7 days vs. 12.8% 7–28 days vs. 29.3% >28 days; p < 0.001). HTD occurred in 15/32 patients, most of whom (10/15) were affected just at baseline. Patients with transient or persistent overexposure trended toward a higher risk of HTD compared to those without (33.3% vs. 8.3%; p = 0.11). Conclusions: A real-time TDM-guided approach could be a valuable tool for optimizing isavuconazole exposure, especially whenever dealing with obese patients or with prolonged treatment. Full article

Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article

Surface electromyographic (sEMG) signal-driven joint-angle estimation plays a critical role in intelligent rehabilitation systems, as its accuracy directly affects both control performance and rehabilitation efficacy. This study proposes a continuous elbow joint angle estimation method based on time–frequency domain analysis. Raw sEMG signals were processed using the Short-Time Fourier Transform (STFT) to extract time–frequency features. A Scale Temporal–Channel Cross-Encoder (STCCE) network was developed, integrating temporal and channel attention mechanisms to enhance feature representation and establish the mapping from sEMG signals to elbow joint angles. The model was trained and evaluated on a dataset comprising approximately 103,000 samples collected from seven subjects. In the single-subject test set, the proposed STCCE model achieved an average Mean Absolute Error (MAE) of $2.96\pm {0.24}^{\circ }$, Root Mean Square Error (RMSE) of $4.41\pm {0.45}^{\circ }$, Coefficient of Determination ($R^2$) of $0.9924\pm 0.0020$, and Correlation Coefficient (CC) of $0.9963\pm 0.0010$. It achieved a MAE of ${3.30}^{\circ }$, RMSE of ${4.75}^{\circ }$, $R^2$ of 0.9915, and CC of 0.9962 on the multi-subject test set, and an average MAE of $15.53\pm {1.80}^{\circ }$, RMSE of $21.72\pm {2.85}^{\circ }$, $R^2$ of $0.8141\pm 0.0540$, and CC of $0.9100\pm 0.0306$ on the inter-subject test set. These results demonstrated that the STCCE model enabled accurate joint-angle estimation in the time–frequency domain, contributing to a better motion intent perception for upper-limb rehabilitation. Full article

This study introduces a no-code educational platform created to introduce Internet of Things (IoT) to university students who lack programming experience. The platform allows users to set IoT sensor nodes, and create a wireless sensor network through a simple graphical interface. Sensors’ data can be sent to cloud services but they can also be stored locally, which makes our platform particularly realistic in fieldwork settings where internet access may be limited. The platform was tested in a pilot activity within a university course that previously covered IoT only in theory and was evaluated using the Technology Acceptance Model (TAM). Results showed strong student engagement and high ratings for ease of use, usefulness, and future use intent. These findings suggest that a no-code approach can effectively bridge the gap between IoT technologies and learners in non-engineering fields. Full article

Power system infrastructure is exposed to a range of threats, including both naturally occurring events and intentional attacks. Traditional vulnerability assessment models, typically based on the N-1 criterion, do not account for the intentionality of disruptive agents. This paper presents a game-theoretic approach to protecting power system infrastructure against deliberate attacks, taking into account the effects of demand response. The interaction between the disruptive agent and the system operator is modeled as a leader–follower Stackelberg game. The leader, positioned in the upper-level optimization problem, must decide which elements to render out of service, anticipating the reaction of the follower (the system operator), who occupies the lower-level problem. The Stackelberg game is reformulated as a bilevel optimization model and solved using a metaheuristic approach. To evaluate the applicability of the proposed method, a 24-bus test system was employed. The results demonstrate that integrating demand response significantly enhances system resilience, compelling the disruptive agent to adopt alternative attack strategies that lead to lower overall disruption. The proposed model serves as a valuable decision-support tool for system operators and planners seeking to improve the robustness and security of electrical networks against disruptive agents. Full article

Background: Overtime adversely affects physical and mental health, contributing to irritability, anxiety, reduced sleep, and even cardiovascular issues, ultimately lowering care quality and increasing turnover intentions. This study aimed to investigate whether prolonged overtime increases the risk of occupational burnout over time among healthcare workers. Methods: We conducted a four-year longitudinal observational study using secondary data from annual surveys (2021–2024) of healthcare workers at a medical university hospital in Taichung, Taiwan. Burnout was assessed using the personal burnout (PB) scale from the Copenhagen Burnout Inventory (CBI), with high PB levels (HPBL) defined as scores in the upper quartile of the 2021 baseline. Survival analysis utilizing the Kaplan–Meier method and Cox regression investigated burnout progression and the effects of overtime. Results: HPBL was defined as PB scores ≥45.83 (upper quartile in 2021). The proportions of HPBL were 30.28% (2021), 33.29% (2022), 36.75% (2023), and 32.51% (2024). Survival analysis confirmed that the risk of burnout increased over time, with the survival time estimated at 2.50 ± 0.03 years and lower survival probabilities observed among participants working overtime (Log-rank test, p < 0.0001). Multivariate logistics revealed overtime work, female gender, being a physician/nurse, and reduced sleep as independent risk factors for HPBL (OR = 3.14 for overtime, p < 0.001). These findings support the hypotheses on burnout progression and the impact of overtime. Conclusions: Overtime significantly heightens the risk of burnout, which worsens over time. Female sex, healthcare roles, obesity, and insufficient sleep are additional risk factors. Limiting overtime and proactive interventions are crucial to preventing burnout in healthcare workers. Full article

Seafood processing byproducts (SB) such as bones and skin can be safely used as food ingredients to increase profitability for the seafood sector and provide nutritional value. An online survey of 716 US adult seafood consumers was conducted to explore SB trial intent, responsiveness to health and safety information, and associated elicited emotions (nine-point Likert scale). Consumers’ SB-elicited emotions were defined as those changing in reported intensity (from a baseline condition) after the delivery of SB-related information (dependent t-tests). As criteria for practical significance, a raw mean difference of >0.2 units was used, and Cohen’s d values were used to classify effect sizes as small, medium, or large. Differences in willingness-to-try, responsiveness to safety and health information, and SB-elicited emotions were found based on self-reported gender and race, with males and Hispanics expressing more openness to SB consumption. SB-elicited emotions were then used to model consumers’ willingness-to-try foods containing SB via logistic regression modeling. Traditional stepwise variable selection was compared to variable selection using raw mean difference > 0.2 units and Cohen’s d > 0.50 constraints for SB-elicited emotions. Resulting models indicated that extrinsic information considered at the point of decision-making determined which emotions were relevant to the response. These new approaches yielded models with increased Akaike Information Criterion (AIC) values (lower values indicate better model fit) but could provide simpler and more practically meaningful models for understanding which emotions drive consumption decisions. Full article

This study developed and validated an integrated theoretical framework combining the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to investigate how driving assistance technologies (DATs) influence risky driving behaviors among young urban Chinese drivers. Based on this framework, we proposed and tested several hypotheses regarding the effects of psychological and technological factors on risky driving intentions and behaviors. A survey was conducted with 495 young drivers in Shaoguan, Guangdong Province, examining psychological factors, technology acceptance, and their influence on risky driving behaviors. Structural equation modeling revealed that the integrated TPB-TAM explained 58.3% of the variance in behavioral intentions and 42.6% of the variance in actual risky driving behaviors, significantly outperforming single-theory models. Attitudes toward risky driving (β = 0.287) emerged as the strongest TPB predictor of behavioral intentions, while perceived usefulness (β = −0.172) and perceived ease of use (β = −0.113) of driving assistance technologies negatively influenced risky driving intentions. Multi-group analysis identified significant gender and driving experience differences. Logistic regression analyses demonstrated that model constructs significantly predicted actual traffic violations and accidents. These findings provide theoretical insights into risky driving determinants and practical guidance for developing targeted interventions and effective traffic safety policies for young drivers in urban China. Full article

Cybersecurity threats increasingly originate from human actions within organizations, emphasizing the need to understand behavioral factors behind non-compliance with information security policies (ISPs). Despite the presence of formal security policies, insider threats—whether accidental or intentional—remain a major vulnerability. This study addresses the gap in behavioral cybersecurity research by developing an integrated conceptual model that draws upon Operant Conditioning Theory (OCT), Protection Motivation Theory (PMT), and the Theory of Planned Behavior (TPB) to explore ISP compliance. The research aims to identify key cognitive, motivational, and behavioral factors that shape employees’ intentions and actual compliance with ISPs. The model examines seven independent variables of perceived severity: perceived vulnerability, rewards, punishment, attitude toward the behavior, subjective norms, and perceived behavioral control, with intention serving as a mediating variable and actual ISP compliance as the outcome. A quantitative approach was used, collecting data via an online survey from 302 employees across the public and private sectors. Structural Equation Modeling (SEM) with SmartPLS software (v.4.1.1.2) analyzed the complex relationships among variables, testing the proposed model. The findings reveal that perceived severity, punishment, attitude toward behavior, and perceived behavioral control, significantly and positively, influence employees’ intentions to comply with information security policies. Conversely, perceived vulnerability, rewards, and subjective norms do not show a significant effect on compliance intentions. Moreover, the intention to comply strongly predicts actual compliance behavior, thus confirming its key role as a mediator linking cognitive, motivational, and behavioral factors to real security practices. This study offers an original contribution by uniting three well-established theories into a single explanatory model and provides actionable insights for designing effective, psychologically informed interventions to enhance ISP adherence and reduce insider risks. Full article

The environmental detriments of the growing global production and overconsumption of beef, including greenhouse gas emissions, deforestation, and biodiversity loss, are well-documented. However, public awareness of how dietary choices affect the environment remains limited. This study examines sustainability views on beef consumption and the potential for behavioral change as a step toward more sustainable intake levels. An observational web-based survey was conducted (n = 1367) to assess respondents’ current beef intake frequency, views on beef consumption related to planetary health, tropical deforestation, greenhouse gas emissions, and climate change, and willingness to modify beef consumption behavior. Chi-square tests were used for group comparisons, and weighted average scores were applied to rank levels of resistance to reducing beef intake. Environmental concern related to beef consumption was associated with greater beef cutback intentions and lower long-term intake reduction resistance amongst beef eaters. Beef eaters who strongly agreed that global beef consumption negatively impacts the environment were considerably more likely to express intentions to reduce their long-term beef intake compared to those who strongly disagreed (94.4% vs. 19.6%). Overall, 76.6% of beef eaters indicated wanting to eat less beef or phase it out entirely (30.7% reduce, 29.4% minimize, 16.6% stop), with only 23.4% of them intending to keep their consumption unchanged. Compelling messages that help translate awareness into action, such as the #NoBeefWeek concept explored in this study, may support individuals in adopting more sustainable food choices. These cross-national findings provide evidence for a ‘knowledge–intent’ gap in sustainable diet research, with relevance for health communicators and policymakers. Future research could examine the factors and motivations influencing decisions to modify beef consumption, including the barriers to achieving sustainable consumption levels and the role of suitable alternatives in facilitating this transition. Full article

Background/Objectives: Misperception of body weight might be associated with the likelihood of being in a pre-contemplation phase and with a limited intention to initiate a healthy behavioral change toward weight management. The current study investigates factors associated with the misperception of body weight, body weight satisfaction, and intentions for body weight modifications. Methods: This study utilized a cross-sectional design to reach adults from Jazan, in the southwest of Saudi Arabia. The data collection tool measured demographics, the participants’ latest height and weight, body weight perception, satisfaction, and intentions concerning body weight modification. Chi-squared tests were used to compare the demographic characteristics between those who had correct perception and those who had a wrong one. Results: A sample of 685 adult participants was included in the current analysis. The mean age of the participants was 31.3 years (standard deviation: 11.1). The proportion of female participants was 52%. Fifty-four percent of the participants had a wrong perception of their body weight. Gender, employment, smoking, khat chewing, having a diagnosed condition, and BMI levels were statistically associated with the perception of body weight (p-values < 0.05). Conclusions: The detected misestimating, especially underestimation, might be associated with the likelihood of participants being in a precontemplation phase and with a limited intention to initiate a healthy behavioral change toward weight management. The practical implications of these findings indicate the importance of incorporating the assessment of weight perception alongside actual BMI measurement in clinical settings. Full article