Search Results (559)

Artificial intelligence (AI) is increasingly used in marketing to deliver personalized messages and services. Although such tools create new opportunities, their acceptance by consumers depends on several factors that go beyond technology itself. This study examines how trust and ethical perceptions, familiarity and exposure to AI, digital consumer behavior, and identity concerns shape acceptance of AI-based personalized advertising. The analysis draws on data from 650 Greek consumers, collected through a mixed-mode survey (online and paper), and tested using logistic regression models with demographic characteristics included as controls. The results show trust and ethical perceptions of acceptance as factors, while familiarity with AI tools also supports positive attitudes once trust is established. In contrast, digital consumer behavior played a smaller role, and identity-related consumption was negatively associated with acceptance, reflecting concerns about autonomy and self-expression. Demographic factors, such as age and income, also influenced responses. Overall, the findings suggest that acceptance of AI in marketing is not only a technical matter but also a psychological and social process. This study highlights the importance for firms to build trust, act responsibly, and design personalization strategies that respect consumer identity and ethical expectations. Full article

Autonomous systems, such as self-driving cars, surgical robots, and space rovers, require efficient and collision-free navigation in dynamic environments. Geometric optimal navigation and path planning have become critical research areas, combining geometry, optimization, and machine learning to address these challenges. This paper systematically reviews state-of-the-art methodologies in geometric navigation and path planning, with a focus on integrating advanced geometric principles, optimization techniques, and machine learning algorithms. It examines recent advancements in continuous optimization, real-time adaptability, and learning-based strategies, which enable robots to navigate dynamic environments, avoid moving obstacles, and optimize trajectories under complex constraints. The study identifies several unresolved challenges in the field, including scalability in high-dimensional spaces, real-time computation for dynamic environments, and the integration of perception systems for accurate environment modeling. Additionally, ethical and safety concerns in human–robot interactions are highlighted as critical issues for real-world deployment. The paper provides a comprehensive framework for addressing these challenges, bridging the gap between classical algorithms and modern techniques. By emphasizing recent advancements and unresolved challenges, this work contributes to the broader understanding of geometric optimal navigation and path planning. The insights presented here aim to inspire future research and foster the development of more robust, efficient, and intelligent navigation systems. This survey not only highlights the novelty of integrating geometry, optimization, and machine learning but also provides a roadmap for addressing critical issues in the field, paving the way for the next generation of autonomous systems. Full article

Architecture students have a limited understanding of technology’s pedagogical benefits, which creates a gap between the potential of technology-enhanced learning and its actual implementation. A promising solution to this underlying problem would be the integration of GAI in architecture education. The purpose of this study is to explore the role of GAI in architecture education from students’ perspectives. A self-developed questionnaire was employed to collect data from 239 architecture students from three universities in Saudi Arabia and Egypt. Frequency distribution and structural equation modeling (SEM) were employed for data analysis. The study found a low level of awareness and moderate level of perception toward GAI. Integrating GAI into architecture education through knowledge and ethical awareness enhances students’ general skills competency and architecture and design expertise. Students who perceive GAI as beneficial enhance their general skills competency, while those who perceive GAI as challenging undermine their architecture and design expertise. The study also reported that students who intend to integrate GAI in architecture education have high ethical awareness toward GAI and possess a positive perception about GAI while inclining toward its benefits. Students should gain a better understanding of GAI tools and the ways to use them in architecture education in order to improve their general and field-specific skills proficiency. Educators must work with students to enhance their knowledge about GAI and the perception of its benefits and challenges, so that a focused skills development can transform students’ basic competencies to advanced architecture and design expertise. Full article

Understanding the factors that influence academic engagement and the perception of ethical–practical suitability is essential for improving university training processes in Social Work. In this context, academic satisfaction plays a key role. This study, with a cross-sectional design and a structural equation modelling (SEM) approach, aimed to examine the direct and indirect relationships among academic engagement, ethical–practical suitability, and academic satisfaction in a sample of Social Work students in Chile. A total of 298 Social Work students participated in this study, from 9 public and private universities (23.1% men, 76.9% women), with a mean age of 21.74 years (SD = 3.470). The results, obtained from a structural equation model, confirm that academic satisfaction significantly and partially mediates the relationship between ethical–practical suitability and academic engagement. Likewise, positive direct effects were observed among the three variables. Confirming a partial mediating effect of academic satisfaction in the relationship between professional suitability and academic engagement. The results are discussed in terms of their relevance for designing training strategies aimed at strengthening academic engagement and the perception of professional readiness in higher education. Full article

Food loss and food waste (FLFW) constitute a major global challenge with profound economic, environmental, and social consequences. This study examines how businesses in Greece’s food service and retail sectors perceive and manage food waste, integrating the triple bottom line framework—economic, environmental, and social sustainability—as the guiding analytical lens. The research aims to: (1) analyze perceptions, practices, and barriers to food waste reduction among businesses; (2) explore the relationship between awareness, business policies, technological adoption, and consumer-oriented strategies; and (3) situate the Greek experience within broader European and international contexts to identify transferable lessons for policy and business innovation. Drawing on a structured survey of 250 industry representatives and comparative international evidence, the study finds that although awareness of food waste is widespread, it remains weakly connected to structured policies, technology adoption, or operational practices. Businesses recognize economic opportunities in waste reduction—such as supply chain optimization and near-expiry discounting—but these remain underexploited due to a lack of strong regulatory and financial incentives. The findings highlight that addressing food waste is not only an environmental and ethical necessity but also a strategic opportunity to enhance economic resilience, competitiveness, and sustainability within the agri-food sector. Full article

Background: This study characterizes the current representation of sociodemographic groups within Canadian radiation oncology training programs and trainees’ lived experiences. Methods: A 59-item ethics-approved, bilingual survey assessed sociodemographics, training perceptions, mentorship, discrimination/harassment experienced, and open-ended questions. Electronic surveys were distributed to all Canadian radiation oncology residents/fellows. Descriptive statistics summarized survey responses. Categorical groups were compared using chi-squared/Fisher’s exact tests. Thematic analysis was performed on open-ended responses. Results: Between July and December 2023, 98 of 177 (56%) trainees participated: 70% were residents, 52% identified as male, 62% as a racialized minority, and 10% as a sexual minority. Most respondents reported training program satisfaction (83%) and a respectful workplace culture (69%); however, discrimination during training was reported by 38%. Less than half (45%) felt comfortable reporting discrimination/harassment within their workplace. Women were more likely to feel under-represented in-training (46% vs. 13%, p = 0.001) and perceived more discrimination events (64% vs. 19%, p < 0.001). Three themes emerged as follows: importance of offering EDI education, ensuring pathways for reporting learner mistreatment, and creating appropriately diverse selection committees. Conclusions: Although most Canadian radiation oncology trainees reported satisfaction and a respectful culture, key differences between groups were observed. Targeted strategies and stronger institutional policies to improve representation and reduce rates of discrimination/harassment are needed. Full article

This paper explores the concept of alterity within “ethical” meat production and consumption, focusing on small-scale producers and consumers in a rural Mediterranean context. Drawing on a relational understanding of alterity as a process through which actors negotiate difference within capitalist foodscapes, the study examines how ethical commitments and socio-material practices co-produce what counts as “alternative.” Using qualitative interviews with producers of “ethical meat” and their consumers from a solidarity-based Alternative Food Network (AFN) in Le Marche (Italy), the paper investigates how producer–consumer relations, animal welfare, and ecological care are interlinked within an ethical meatscape. Despite differing perceptions of the AFN’s role, embodied exchanges and face-to-face trust emerge as key to sustaining ethical and environmentally conscious practices. By capturing the micro-social dynamics through which relational alterity is enacted, the paper contributes to debates on sustainable agri-food transitions and socio-environmental justice, showing how ethical and ecological outcomes are co-produced through human and more-than-human relations. Full article

This study explores the effectiveness of artificial intelligence-generated videos (AIGV) as a scalable enabling technology within the e-commerce sector. It investigates the potential of AIGV to enhance marketing efficacy through the simulation of product experiences, with a particular focus on space tourism. A notable gap exists in the current understanding of how the attributes of AIGV and individual perceptions influence critical consumer responses in the context of space tourism e-commerce. This gap specifically pertains to their effects on trustworthiness, awe, and behavioral intentions, with an emphasis on the underlying mediating mechanisms. Purposive sampling was employed to gather samples, and partial least squares structural equation modeling (PLS-SEM) was utilized for data analysis. The results reveal that both AIGV attributes and personal perceptions exert a significant influence on trustworthiness, awe, and behavioral intentions within the context of space tourism e-commerce. Awe serves as a central mediating construct between AIGV attributes and behavioral intention, while also mediating the relationship between perceived risk and behavioral intention. In contrast, trustworthiness solely mediates the pathway between perceived risk and behavioral intention. The findings present novel theoretical insights into AI-driven consumer behavior within experiential e-commerce contexts. They also offer practical guidance for the effective implementation of the AIGV. Moreover, this study underscores the necessity for ethical frameworks to regulate consumer trust in AI-dominated marketplaces. Full article

With the digital transformation of the construction industry toward intelligent construction, advanced digital technologies—including Artificial Intelligence (AI), Digital Twins (DTs), and Internet of Things (IoT)—increasingly support Human–Robot Collaboration (HRC), offering productivity gains while introducing new safety risks. This study presents a systematic review of digital technology applications and risk management practices in HRC scenarios within intelligent construction environments. Following the PRISMA protocol, this study retrieved 7640 publications from the Web of Science database. After screening, 70 high-quality studies were selected for in-depth analysis. This review identifies four core digital technologies central to current HRC research: multi-modal acquisition technology, artificial intelligence learning technology (AI learning technology), Digital Twins (DTs), and Augmented Reality (AR). Based on the findings, this study constructed a systematic framework for digital technology in HRC, consisting of data acquisition and perception, data transmission and storage, intelligent analysis and decision support, human–machine interaction and collaboration, and intelligent equipment and automation. The study highlights core challenges across risk management stages, including difficulties in multi-modal fusion (risk identification), lack of quantitative systems (risk assessment), real-time performance issues (risk response), and weak feedback loops in risk monitoring and continuous improvement. Moreover, future research directions are proposed, including trust in HRC, privacy and ethics, and closed-loop optimization. This research provides theoretical insights and practical recommendations for advancing digital safety systems and supporting the safe digital transformation of the construction industry. These research findings hold significant important implications for advancing the digital transformation of the construction industry and enabling efficient risk management. Full article

Israeli higher education institutes are challenged by the growing number of ultra-orthodox students. This requires coping with novel aspects unfamiliar to participants, as students and as teachers in the education system, utilizing online learning as a lever for empowering this marginalized population. The aim of the proposed research was to explore perceptions of ultra-orthodox students studying in B.Ed. programs within a secular college of education towards online courses. Data included transcriptions from 68 narratives of interviews, which were analyzed using a mixed-methods approach, which helped us achieve an in-depth understanding of the difficulties and challenges of these higher education students. Altogether, five themes were identified, namely: technical challenges, ethical/religious challenges, academic challenges, engagement challenges, and aspects of availability. Statements referring to academic challenges and engagement challenges were the most frequent. The number of positive and negative statements was balanced. Also, distinct patterns of responses were identified for married vs. single ultra-orthodox women. Findings demonstrate the complexity of utilizing online learning among ultra-orthodox B.Ed. students, in a twofold manner: personally and community-wise. The study may shed light on online learning in additional marginal communities worldwide that are traditional in nature, and that may benefit from online courses. Full article

Digital marketing has become a game-changer by combining cutting-edge technologies, insights into how customers behave, and applicability across industries to change how businesses plan and how they interact with customers. Digital marketing is a key part of being competitive, sustainable, and innovative in a world where more and more people are using the internet and social media. Even though this subject is important, the study of it is still scattered, which shows that there is a need to systematically map out its intellectual structure. This research utilizes a bibliometric and topic modeling methodology, analyzing 4722 publications sourced from the Scopus database, including the string “Digital Marketing”. The authors employed Latent Dirichlet Allocation (LDA), a method from Natural Language Processing, to discern latent study themes and Vosviewer 1.6.20 for bibliometric analysis. The results explore ten main thematic clusters, such as digital marketing and blockchain, applications in the health and food industries, higher education and skill enhancement, machine learning and analytics, small and medium-sized enterprises (SMEs) and sustainability, emerging trends and ethics, sales transformation, tourism and hospitality, digital media and audience perception, and consumer satisfaction through service quality. These clusters show that digital marketing is becoming more interdisciplinary and is becoming more connected to ethical and technological issues. The report finds that digital marketing research is changing quickly because of artificial intelligence (AI), blockchain, immersive technology, and reflect it with a digital business environment. Future directions encompass the expansion of analyses to new economies, the implementation of advanced semantic models, and the navigation of ethical difficulties, thereby guaranteeing that digital marketing fosters both business progress and public welfare. Full article

In the digital age, generative artificial intelligence (GenAI) development has brought about structural transformations in higher education. This study examines how students’ regular use of artificial intelligence tools brings a new active player into the educational process. This is an “uninvited assistant” that changes traditional models of teaching and learning. This study was conducted using the following standard methods: bibliometric analysis, student survey using an electronic questionnaire, primary processing and graphical visualization of empirical data, calculation of statistical indicators, t-statistics, and z-statistics. As the results of the bibliometric analysis show, the evolution in the perception and integration of artificial intelligence within higher education discussions, as evidenced by the comparison of network visualizations from 2020 to the present, reveals a significant transformation. Based on a quantitative survey of 1197 undergraduate students in five Eastern European countries, this paper proposes a conceptual shift from the classic two-dimensional (2D) model of higher education services based on university teacher–student interactions to a three-dimensional (3D) model that includes artificial intelligence as a functional third player (an uninvited assistant). Statistical hypothesis testing confirms that students need AI and regularly use it in the learning process, facilitating the emergence of this new player. Based on empirical data, this study presents a hypothetical 3D model (X:Y:Z), where the Z-axis reflects the intensity of AI use. This model challenges traditional didactic frameworks and calls for updating educational policies, ethical standards, and higher education governance systems. By merging digital technologies and social change, the results provide a theoretical and practical basis for rethinking pedagogical relationships and institutional roles in the digital age. Full article

The rapid emergence of Generative Artificial Intelligence (GenAI) has sparked a growing debate about its ethical, methodological, and epistemological implications for qualitative research. This study aimed to examine and deeply understand researchers’ perceptions regarding the use of GenAI tools in different phases of the qualitative research process. The study involved a sample of 214 researchers from diverse disciplinary areas, with publications indexed in Web of Science or Scopus that apply qualitative methods. Data collection was conducted using an open-ended questionnaire, and analysis was carried out using coding and thematic analysis procedures, which allowed us to identify patterns of perception, user experiences, and barriers. The findings show that, while GenAI is valued for its ability to optimize tasks such as corpus organization, initial coding, transcription, translation, and information synthesis, its implementation raises concerns regarding privacy, consent, authorship, the reliability of results, and the loss of interpretive depth. Furthermore, a dual ecosystem is observed, where some researchers already incorporate it, mainly generative text assistants like ChatGPT, while others have yet to use it or are unfamiliar with it. Overall, the results suggest that the most solid path is an assisted model, supported by clear ethical frameworks, adapted methodological guidelines, and critical training for responsible and humanistic use. Full article

Background: Effective shared decision-making (SDM) for life-sustaining treatment (LST) requires alignment between physicians and surrogates. However, discrepancies in perceptions and communication may hinder ethically sound decisions. This study aimed to compare the perceptions and attitudes of physicians and surrogates toward SDM for LST in a Chinese hospital setting. Methods: This pre-planned secondary analysis included data from two cross-sectional surveys administered to physicians and surrogates. Participants were 325 surrogates of critically ill adult patients admitted to the Emergency Intensive Care Unit (EICU) of a tertiary teaching hospital and 351 physicians from hospitals in Beijing. Survey items assessed triggers and preferred models of decision-making, disclosure practices, perceived decisional capacity, and factors influencing LST decisions. Statistical comparisons were performed using appropriate tests for categorical data. Results: Although a majority in both groups nominally preferred SDM (physicians: 52.7%; surrogates: 44.3%; p = 0.155), significant discrepancies emerged across other domains. Physicians were more likely than surrogates to initiate LST discussions earlier (88.0% vs. 75.3%; p < 0.001). Perceived understanding differed markedly: 87.7% of surrogates rated their comprehension as “good” or “excellent”, whereas 73.8% of physicians rated surrogate understanding as “fair” or “poor” (p < 0.001). Surrogates expressed a stronger preference for receiving quantitative prognostic information and decision-support tools. Most physicians (94.9%) reported directing consent discussions primarily to families, with limited patient involvement. Priorities for LST decisions diverged: physicians emphasized clinical indicators such as prognosis (96.0%) and comorbidities (91.7%), whereas surrogates emphasized patient age (72.0%). Conclusions: Marked discordances exist between physicians and surrogates in their perceptions and practices regarding SDM for LST in China. Differences in communication strategies, informational expectations, and decision-making priorities underscore the need for contextually adapted interventions, such as structured communication tools and culturally sensitive clinician training, to bridge these gaps and support ethically aligned decision-making. 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