Search Results (3,347)

Artificial intelligence (AI) is reshaping customer service, with AI-powered chatbots serving as a critical component in delivering continuous support across sales, marketing, and service domains, thereby enhancing operational efficiency. However, consumer engagement remains suboptimal, as many users favor human interaction due to concerns regarding chatbots’ ability to address complex issues and their perceived lack of empathy, which subsequently reduces satisfaction and sustainable usage. This study examines the determinants of user attitude and identifies factors influencing sustainable chatbot use. Utilizing survey data from 735 Chinese university students who have engaged with AI-powered chatbots, the analysis reveals that four key motivational categories: utilitarian (information acquisition), hedonic (enjoyment and time passing), technology (media appeal), and social (social presence and interaction) significantly influence user attitude toward chatbot services. Conversely, privacy invasion exerts a negative impact on user attitude, suggesting that while chatbots provide certain benefits, privacy issues can significantly undermine user satisfaction. Moreover, the findings suggest that user attitude serves as a pivotal determinant in fostering both user stickiness and sustainable usage of chatbot services. This study advances prior U&G-, TAM-, and ECM-based research by applying these frameworks to AI-powered chatbots in business communication, refining the U&G model with four specific motivations, integrating perceived privacy invasion to bridge gratification theory with risk perception, and directly linking user motivations to business outcomes such as attitude and stickiness. This study underscores that optimizing chatbot functionalities to enhance user gratification while mitigating privacy risks can substantially improve user satisfaction and stickiness, offering valuable implications for businesses aiming to enhance customer loyalty through AI-powered services. Full article

This perspective article presents an innovative concept for a biomanufacturing Knowledge Hub (KH), designed as a data-driven learning platform supporting the entire lifecycle of biomedical products. By integrating advanced data sharing and processing technologies, the KH aspires to connect patients, bioengineers, clinicians, regulators, companies, and investors to accelerate product development, reduce redundancies, and ultimately fast-track the delivery of biomedical innovations to patients. We discuss current challenges in accessing and sharing data within biomanufacturing and outline novel approaches for building an ecosystem that links data stores, integrates digital twins, and leverages advanced analytics. The KH offers transformative capabilities, enabling the development of new products at a substantial increased speed. It is built as a secure, quantum-resistant platform that encrypts data and allows access through advanced algorithms, creating an intelligent, collaborative environment. Users can harness collective knowledge to enhance products, launch innovations, integrate technologies, and unlock revenue opportunities based on data quality and usage. This KH aims to revolutionize biomanufacturing, offering unprecedented opportunities for innovation, better patient outcomes, and commercialization with far reaching applications beyond biomanufacturing in the future. Full article

The world’s energy system faces major challenges due to transitions from fossil fuels to other alternatives. An important part of the transition is energy-efficient homes that partially produce their own electricity. This paper explores the energy interactions between heating, cooling, and electricity usage in a planned residential area in Sweden where a significant portion of the electricity is generated by solar PV systems. Conventional district heating and cooling systems and a low-temperature district heating system that uses return cascading technology were compared with heat pump systems. Electricity sharing in an energy community has a low impact on the calculated national energy efficiency metric. It is also shown that electrifying space heating with heat pumps improves the calculated energy efficiency metric, but heat pumps increase the peak power demand in the winter due to high heat demand and a lack of solar production. Using heat pumps for heating domestic hot water and compressor chillers for cooling offers a more balanced use/production of electricity since the electric cooling load is mostly met by local solar production, as shown by an increase in self-consumption of 8% and stable self-sufficiency. There is, however, a time mismatch between production and the peak electricity demand, which could be addressed by using energy storage systems. Full article

We explored the adoption of virtual try-on (VTO) technology in Taiwan’s fashion retail sector, which has gained prominence as consumer behavior online has changed since the COVID-19 pandemic. Using an extended unified theory of acceptance and use of technology 2 (UTAUT2) model, we examined the behavioral intentions and actual usage of VTO. The original framework of the UTAUT2 model was modified by excluding experience and incorporating personality traits as moderating variables. Based on 257 valid survey responses analyzed using SmartPLS 4.1, influencing factors were identified, revealing that gender was a significant moderator in VTO adoption. Full article

Background/Objectives: While dentistry plays a critical role in promoting oral health, it also contributes significantly to environmental degradation through high energy consumption, water usage, and reliance on disposable, non-recyclable materials. Periodontology, in particular, involves resource-intensive procedures such as full-mouth disinfection, frequent surgical interventions, and aerosol-generating instrumentation. The aim of the present narrative review is to synthesize current knowledge and delineate feasible, evidence-informed strategies to operationalize sustainability across the full spectrum of periodontal treatment settings. Methods: The electronic search of the present narrative review was performed across PubMed/MEDLINE, Web of Science, BioMed Central, Scopus, CINAHL, and Cochrane Library databases. Results: The review identified actionable sustainability strategies across pre-workplace (e.g., eco-conscious procurement and transport reduction), workplace (e.g., energy- and water-saving technologies, digital workflows, and pollution control), and waste management (e.g., reuse protocols, recycling, and sustainable material selection). Particular emphasis was placed on the role of dental education, life cycle assessments, and digital innovations. Conclusions: The transition toward sustainable periodontology requires the adoption of evidence-based practices and leveraging digital innovation to reduce the environmental impact while maintaining high standards of care. Full article

Generative artificial intelligence (GenAI) is advancing rapidly and is increasingly integrated into educational settings. How to effectively leverage GenAI to support instructional design has thus become a critical issue in teacher education. While existing studies have validated the technical potential and functional value of GenAI in instructional design, there remains a notable gap in qualitative investigations into pre-service teachers’ subjective willingness to adopt GenAI and its underlying influencing factors. To address this gap, this present study employed grounded theory to explore the factors that shape pre-service teachers’ intention to use GenAI for instructional design. Semi-structured interviews were conducted with 23 pre-service teachers from Shaanxi Normal University, and the data were analyzed through open coding, axial coding, and selective coding. A theoretical model comprising four major dimensions was developed as follows: (1) technical factors (relative advantage and ease of use), (2) environmental factors (social impact, opinion leader, and facilitating conditions), (3) usage characteristics (purpose of use and method of use), and (4) psychological factors (trust, perceived risk, and a professional self-concept). The findings reveal that pre-service teachers’ intention to use GenAI is not shaped by a single factor but is instead the result of dynamic and interrelated interactions among the four dimensions. This study extends current technology acceptance theories and offers practical insights for the effective integration and promotion of GenAI in instructional design. Full article

This study investigates how students of English Language and Literature Studies and those of Translation at the University of Maribor, Slovenia, perceive and engage with digital tools in academic and language learning contexts. Although students report high levels of confidence in their digital skills and express positive attitudes towards educational technologies, the survey results reveal a significant gap between perceived competence and actual usage. The study identifies the underutilization of institutional tools, limited awareness of resources available, and a reliance on general-purpose search engines rather than academic platforms. These findings highlight the need for improved digital literacy training, structured onboarding, and integration of digital tools into discipline-specific curricula. By focusing on a student population specializing in linguistics and translation in a Central and Eastern European context, this research contributes a localized perspective to broader discussions on digital transformation in higher education. The study offers applicable recommendations for enhancing institutional strategies and supporting students in becoming competent and critical users of educational technology. Full article

Against the backdrop of the low-altitude economy, with the widespread application of drones in sports scenarios, the driving mechanism of users’ long-term usage intention has become a key issue in technology adoption research. To investigate the critical factors influencing the continuous use of drone products by sports-involved populations, this study builds a factor model for users’ continuous use of drones. It is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, integrating the UTAUT2 model and specific user needs in sports scenarios. Both traditional structural equation modeling (SEM) and Bayesian structural equation modeling (BSEM) are employed for empirical testing. Through the analysis of 297 valid questionnaire responses, it is found that the Bayesian approach yields a better fit. Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Safety and Environmental Compatibility all exert significant positive impacts on users’ continuous usage intention, with Effort Expectancy having the most prominent influence. On this basis, service strategies for drone brands are proposed to support product design and service provision. This study preliminarily indicates that Bayesian analysis possesses advantages and potential in this field. Meanwhile, the factor model for users’ long-term drone usage can meet the development needs in sports scenarios, and it has strong feasibility as a design model for users’ long-term drone usage. Full article

The reuse of treated wastewater (TWW) offers a sustainable solution for water management in agriculture, particularly in arid regions like Saudi Arabia. However, its success depends on farmers’ acceptance, influenced by perceptions of economic benefits, social acceptability, environmental impacts, and health risks. This study surveys 391 farmers across five regions in Saudi Arabia to assess their attitudes toward TWW reuse in irrigation, exploring how advanced wastewater treatment technologies can improve acceptance. Results show that 65% of farmers use TWW, with usage peaking at 72% in Al-Ahsa and Qatif, driven by water scarcity and lack of alternatives. While 78% are satisfied with TWW, concerns persist regarding pests, consumer acceptance, health risks, and soil quality. Advanced technologies can mitigate these issues by enhancing water quality and safety. The highest positive impact of the use of TWW in irrigation from was the impact on productivity, reduction in the cost of fertilizers and savings in the cost of water abstraction. With only 57% of farmers receiving extension services, integrating education on these technologies could further boost confidence. This study highlights key acceptance factors, underscoring the need for technological and educational interventions to promote sustainable TWW reuse in agriculture. Full article

This narrative review has examined the psychological and emotional effects of non-consensual distribution of intimate images, a phenomenon known as Image-Based Sexual Abuse (IBSA). It is an increasingly frequent phenomenon, and in detail, it consists of when a person uploads nude/semi-nude photos of someone online; therefore, it is similar to nonconsensual pornography. According to the scientific literature, this phenomenon has caused victims to experience trust issues, anxiety, depression, suicidal thoughts, social withdrawal, public shame and humiliation, depression, fear, anxiety, stress, inability to find new romantic partners, and several other mental health effects. The aim of this study was to study the psychological consequences of Image-Based Sexual Abuse on victims. This study is a narrative review conducted in accordance with the SANRA guidelines. Findings have shown that revenge pornography and non-consensual intimate images have had devastating impacts on victims’ mental health. Full article

Frequent disruptions to global supply chains, driven by factors such as trade restrictions and geopolitical conflicts, brought supply chain resilience to the forefront of both academic research and industry practice. Concurrently, the rapid advancement of artificial intelligence (AI) technologies in supply chain management in recent years offers new perspectives for researching resilience. Based on the Organizational Information Processing Theory (OIPT), this study explores the direct and indirect mechanisms through which AI usage impacts supply chain resilience from an information processing perspective. Within the OIPT framework, we develop a theoretical model incorporating AI usage, supply chain resilience, supply chain efficiency, supply chain collaboration, and digital information technology capability. We empirically test the model using survey data collected from 231 Chinese manufacturing senior executives and supply chain managers, employing partial least squares structural equation modeling (PLS-SEM). The findings reveal that AI usage has a significant direct positive effect on supply chain resilience. Additionally, supply chain efficiency and collaboration act as mediators in this relationship. Furthermore, we examined the moderating role of a firm’s digital information technology capability and found that it positively moderates the impact of AI usage on supply chain resilience. Full article

In recent years, there has been a fast expansion in the usage of renewable energy sources (RESs) in power distribution systems. Numerous advantages result from this advancement, such as environmental friendliness, cost-effective power generation, easier maintenance, and energy sustainability and reliability. Reducing reliance on fossil fuels, which are of significant environmental concern, and increasing energy efficiency are two benefits of integrating RESs into maritime systems, such as port microgrids. As a result, ports are implementing several programs to increase energy efficiency using various RESs that are supported by power electronic converters. To highlight the most recent developments in seaport electrification and infrastructure, this work conducts a systematic review. It addresses important issues like energy efficiency enhancements, environmental concerns, the integration of renewable energy sources, the Internet of Things (IoT), and regulatory and legal compliance. The study also discusses technology strategies like digitization, electrification, onshore power supply systems, and port energy storage options. Operational tactics, including peak-shaving methods and energy-efficient operations, are also covered. Additionally, an infrastructure framework—which includes port microgrids and smart seaport microgrids—that is intended to enhance energy efficiency in contemporary ports is examined. Full article

Background/Objectives: Artificial intelligence (AI) tools for fracture detection in radiographs are increasingly approved for clinical use but remain underutilized. Understanding physician attitudes before implementation is essential for successful integration into emergency care workflows. This study investigates the acceptance of an AI-based fracture detection tool among physicians in emergency care settings, using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Methods: A cross-sectional, pre-implementation survey was conducted among 92 physicians across three hospitals participating in the SMART Fracture Trial (ClinicalTrials.gov: NCT06754137). The questionnaire assessed the four core UTAUT constructs—performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC)—and additional constructs such as attitude toward technology (AT), diagnostic confidence (DC), and workflow efficiency (WE). Responses were collected on a five-point Likert scale. Structural equation modeling (SEM) and confirmatory factor analysis (CFA) were performed to assess predictors of behavioral intention (BI). Results: PE was the strongest predictor of BI (β = 0.5882, p < 0.001), followed by SI (β = 0.391, p < 0.001), FC (β = 0.263, p < 0.001), and EE (β = 0.202, p = 0.001). These constructs explained a substantial proportion of variance in BI. WE received the lowest ratings, while internal consistency for SI and BI was weak. Moderator analyses showed prior AI experience improved EE, whereas more experienced physicians were more skeptical regarding WE and DC. However, none of the moderators significantly influenced BI. Conclusions: Physicians’ intention to use AI fracture detection is primarily influenced by perceived usefulness and ease of use. Implementation strategies should focus on intuitive design, targeted training, and clear communication of clinical benefits. Further research should evaluate post-implementation usage and user satisfaction. Full article

Location-based games offer innovative approaches for health promotion among older adults, but their effectiveness depends on understanding complex contextual factors beyond technological design. In our study, we aimed to adapt a location-based game in the form of a smartphone application which originally targeted younger people. We employed ethnographic observations in a field test under real-world conditions for identifying the needs and preferences of older adults in this regard. Field notes of one co-creative workshop were analyzed using thematic analysis. Four key contextual factor categories emerged that significantly influenced user engagement: (1) temporal/spatial factors including weather conditions, topography, and traffic safety that impacted screen visibility and cognitive function; (2) virtual-physical orientation challenges requiring high cognitive load to transfer abstract digital maps to real environments; (3) individual factors such as technical competence, mobility levels, and prior accessibility experiences that shaped usage patterns; and (4) social dynamics that provided motivation and peer support while potentially creating exclusionary practices. Successful digital health innovations for older adults require a socio-technical systems approach that addresses environmental conditions, reduces cognitive transfer demands between virtual and physical navigation, leverages social elements while preventing exclusion, and accounts for heterogeneity among older adults as contextually interactive factors rather than merely individual differences. Full article

With the advancement of refined urban governance and the construction of high-quality public spaces, street furniture design and usage face multiple challenges, including insufficient public participation and a neglect of actual user experience. These issues highlight the urgent need to establish a scientifically grounded user evaluation framework to inform design practices. This study focuses on Yangzhou Ecological Science and Technology New Town and, drawing on field investigation, grounded theory, and the Delphi method, develops a street furniture design evaluation framework encompassing three core dimensions: planning and configuration, environmental coordination, and operational management. Building on this framework, the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation method are employed to conduct a holistic assessment of the street furniture and to identify critical design deficiencies. The results demonstrate that the proposed framework effectively identifies the strengths and weaknesses of street furniture and provides robust support for formulating targeted optimization strategies. The results reveal significant variations in the perceived importance of design factors among different user groups. Residents primarily emphasize practicality and convenience in daily use. Tourists value aesthetic expression and cultural resonance, whereas government officials focus on construction standardization and maintenance efficiency. In terms of satisfaction, all three groups reported relatively low scores, with the ranking as follows: “planning and configuration” > “management and operations” > “environmental coordination.” Based on these findings, the study proposes targeted design guidelines for future practice. The evaluation framework has been adopted by local authorities, incorporated into official street furniture design guidelines, and implemented in pilot projects—demonstrating its practical applicability and value. This research contributes to the theoretical advancement of street furniture design and provides empirical and methodological support for applications in other emerging urban areas and new town developments. Full article