Search Results (2,074)

This study investigates the perception-to-purchase journey by examining how consumer artificial intelligence (AI) literacy influences the effectiveness of AI-generated sponsored vlogs (AISVs), an emerging digital marketing format. Using survey data from 413 consumers and structural equation modeling, we develop and test the AI Literacy Perception–Decision Model (AILPDM). Results show that AI literacy affects information adoption through three pathways: emotional value, information usefulness, and source credibility. Separate SEM analyses further suggest that the direct effect of AI literacy on purchase intention was observed in experiential service AISVs, whereas in tangible product AISVs the effect operated mainly through information adoption. The AILPDM framework advances marketing theory by tracing a decision pathway from AI literacy, through perceived value and information adoption, to purchase intention, thereby demonstrating how technological competence evolves from a cost barrier into a cognitive resource that shifts source credibility evaluation from peripheral to central processing. For practitioners, the findings suggest differentiated strategies: Marketers of experiential services should emphasize anthropomorphic elements, whereas marketers of tangible products should prioritize technological transparency to foster consumer trust. Full article

Reliable schedule-risk estimation in hybrid software development lifecycles is strategically important for organizations adopting AI in software engineering. This study addresses that need by transforming routine process telemetry (CI/CD, SAST, traceability) into explainable, quantitative predictions of completion time and rework. This paper introduces an integrated probabilistic model of the hybrid software development lifecycle that combines Generalized Evaluation and Review Technique (GERT) network semantics with I-AND synchronization, explicit artificial-intelligence (AI) interventions, and a fuzzy treatment of epistemic uncertainty. The model embeds two controllable AI nodes–an AI Requirements Assistant and AI-augmented static code analysis, directly into the process topology and applies an analytical reduction to a W-function to obtain iteration-time distributions and release-success probabilities without resorting solely to simulation. Epistemic uncertainty on critical arcs is represented by fuzzy intervals and propagated via Zadeh’s extension principle, while aleatory variability is captured through stochastic branching. Parameter calibration relies on process telemetry (requirements traceability, static-analysis signals, continuous integration/continuous delivery, CI/CD, and history). A validation case (“system design → UX prototyping → implementation → quality assurance → deployment”) demonstrates practical use: large samples of process trajectories are generated under identical initial conditions and fixed random seeds, and kernel density estimation with Silverman’s bandwidth is applied to normalized histograms of continuous outcomes. Results indicate earlier defect detection, fewer late rework loops, thinner right tails of global duration, and an approximately threefold reduction in the expected number of rework cycles when AI is enabled. The framework yields interpretable, scenario-ready metrics for tuning quality-gate policies and automation levels in Agile/DevOps settings. Full article

In recent years, the concept of artificial intelligence (AI) has garnered increasing scholarly and professional interest, particularly regarding its implementation across various domains, including journalism. As with any emerging technological paradigm, AI must be examined within its contextual framework to elucidate its potential advantages, challenges, and transformative implications. This study, situated within the theoretical lens of Actor–Network Theory, employs a mixed methods approach and, specifically, an explanatory sequential design to explore the integration of AI in contemporary Greek journalism. Primary data were collected through a structured questionnaire (N = 148) administered to professional journalists in Greece, followed by semi-structured interviews with a subset of participants (N = 7). The findings indicate that journalists perceive AI as a tool capable of enhancing work efficiency, minimizing human error, and facilitating the processing of unstructured data. However, respondents also expressed concerns that AI adoption is unlikely to lead to improved financial compensation and may contribute to job displacement within the sector. Additionally, participants emphasized the necessity of regular professional development initiatives, advocating for the organization of seminars on emerging technologies on a biannual or annual basis. Full article

Background: In the evolving landscape of pathology, Laboratory Information Systems (LISs) have become essential tools for ensuring traceability, efficiency, and data security in diagnostic workflows. Methods: This study presents a comprehensive comparative analysis of three major LIS platforms used in Italian pathology laboratories in 2025: Armonia (Dedalus), Pathox Web (Tesi Group), and WinSAP 3.0 (Engineering). Each system is evaluated across key parameters, including sample traceability, integration with hospital systems, digital reporting, user interface, and compliance with regulatory standards such as GDPR and ISO 15189. Results: Armonia stands out for its advanced integration capabilities, scalability, and support for digital pathology, making it ideal for large institutions. Pathox Web offers a balanced solution with strong usability and web-based accessibility, suitable for medium-sized laboratories. WinSAP 3.0, while more limited in modern features, remains a stable and cost-effective option for many facilities. This study emphasizes the strategic importance of selecting an LIS aligned with institutional needs, highlighting its role in enhancing diagnostic quality, operational safety, and future integration with artificial intelligence and automation. Conclusions: The findings support informed decision-making in LIS adoption, critically contributing to the management of scientific and economic data of pathology services in Italy. Full article

Transesophageal echocardiography (TEE) has long been established as a cornerstone imaging modality in cardiac surgery and perioperative medicine. In recent years, however, its role has expanded into emergency and intensive care settings, where rapid and accurate hemodynamic assessment is crucial for survival. Point-of-care TEE provides advantages over transthoracic echocardiography when acoustic windows are limited, particularly in mechanically ventilated or critically unstable patients, allowing continuous high-quality visualization of cardiac function, volume status, and great vessel pathology to guide immediate therapeutic interventions. This narrative review examines the evolving role of TEE in acute settings, with emphasis on its application in shock, cardiac arrest, pulmonary embolism, tamponade, and its value in extracorporeal membrane oxygenation (ECMO) cannulation. Advances such as three-dimensional TEE, miniaturized probes, and the integration of artificial intelligence are also discussed, as potential drivers of innovation. While bridging technological progress with clinical practice, TEE emerges as a versatile tool in critical care. However, its broader adoption is still limited by probe availability, operator training, and institutional resources. Overcoming these barriers will be essential to translating technological advances into widespread practice. Full article

In this study, we review recent research on how digital payment technologies (DPTs) promote financial inclusion (FI) across the world. Drawing on empirical studies from the past decade, we show that digital payment systems have helped reduce financial exclusion—particularly in developing economies—by expanding access to essential financial services for underserved groups. The paper also highlights the role of demographic factors such as age and gender, with evidence of higher adoption among youth and women. We identify the main indicators used to measure digital payment adoption and FI, providing a foundation for future empirical analysis. To deepen understanding, we call for combining macroeconomic data with rigorous econometric approaches to better capture how DPTs contribute to inclusive financial systems. The paper further discusses how emerging innovations—including blockchain, artificial intelligence, cloud computing, and biometric authentication—are improving the efficiency, security, and accessibility of digital payments. Together, these technologies are likely to accelerate the transition toward fully digital financial ecosystems and expand the potential for inclusive and sustainable growth. Full article

Circular economy learning fosters competencies in sustainable resource management and environmental protection, which have been recognized by the OECD (Organization for Economic Cooperation and Development) to be essential for cross-curricular training and higher education. However, implementing gamification techniques through mobile applications remains challenging, as their effectiveness depends on students’ willingness to adopt them. This study evaluated acceptance of a gamified mobile application for circular economy learning among university students in Peru, analyzing the relationships between the constructs of the Technology Acceptance Model (TAM). A quantitative correlational case study involving 76 students was conducted. The results showed a moderate-to-high acceptance rate of 73.69%, with significant correlations identified between the TAM constructs. This study contributes to closing gaps in empirical evidence on the acceptance of technology for sustainability education in diverse contexts. Future studies should integrate generative artificial intelligence into gamified apps to deliver personalized feedback and employ learning analytics tools for progress tracking, supporting global efforts toward SGD 4 (Quality Education) and SDG 12 (Responsible Production and Consumption) for the transition to circular economies. Full article

The application of the same global legal regulations to areas with different climates, landscapes, and cultural and urban conditions may ultimately lead to decisions that are unsuitable for the region, which could result in poor investment and development decisions for the municipality. This article examines how sustainability regulations established locally, in response to local conditions, differ from global regulations created without considering the differences between the areas to which they apply. Selected criteria were assessed in relation to global and local regulations, and then, based on these criteria and their weights, rankings of the strengths and weaknesses of municipalities were proposed in relation to the selected criteria, the weights of which were evaluated depending on the adopted global or local regulations. The AHP method was used to conduct this multi-criteria assessment, based both on expert group opinions and artificial intelligence tools. The aim of this analysis was to demonstrate differences in the hierarchies of sustainable development aspects implemented globally and locally, as well as local conditions. The assessment results indicate discrepancies between expert knowledge, which takes into account local conditions, and the priorities resulting from general legal regulations. Some areas important from a local perspective, such as building density or mixed-use development, are insufficiently addressed in legal regulations, both under Polish and EU law and local law. This also contradicts current trends in urban planning theory, which advocates a shift away from zoning. Others, such as energy efficiency in buildings and renewable energy sources, are strongly present in both national and EU law but are not implemented in local regulations. Full article

Rising financial fraud impacts industries, economies, and consumers, creating a need for advanced technological solutions. Compliance frameworks help detect and prevent illicit activities like money laundering, market manipulation, etc. However, with the rise of cryptocurrencies and blockchain, traditional detection methods are ineffective. As a result, Artificial Intelligence (AI) has emerged as a vital tool for combating fraud in the cryptocurrency sector. This systematic review examines the integration of AI in compliance for cryptocurrency fraud detection between 2014 and 2025, analyzing its evolution, methodologies, and emerging trends. Using RStudio (Biblioshiny) and VOSviewer, 353 peer-reviewed studies from leading databases including SciSpace, Elicit, Google Scholar, ScienceDirect, Scopus, and Web of Science were analyzed following the PRISMA methodology. Key trends include the adoption of machine learning, deep learning, natural language processing, and generative AI technologies to improve efficiency and innovation in fraud detection. However, challenges persist, including limited transparency in AI models, regulatory fragmentation, and limited access to quality data, all of which hinder effective fraud detection. The long-term real-world effectiveness of AI tools remains underexplored. This review highlights the trajectory of AI in compliance, identifies areas for further research, and emphasizes bridging theory and practice to strengthen fraud detection in cryptocurrency transactions. Full article

Invasive insects can cause significant economic impacts to agriculture worldwide and impact human health. Traditional pest management methods that include chemical insecticides have raised increasing environmental and health concerns, prompting the need for sustainable alternatives. The Sterile Insect Technique (SIT), which consists of releasing sterile males of a target pest to mate with wild females, is held as a promising solution. However, the success of SIT relies on the release of sterile males. The efficient separation of sexes prior to sterilization and release is necessary. This study presents SIT-ia, a software–hardware system that utilizes artificial intelligence (AI) and computer vision to automate the sex-sorting process. We showcase its use with the fruit fly pest D. suzukii. The system was able to identify males from females with a 98.6% accuracy, sorting 1000 sterile flies in ~70 min, which is nearly half the time involved in manual sorting by experts (i.e., ~112 min). This simple device can easily be adopted in SIT production protocols, improving the feasibility and efficacy of improved pest management practices. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly applied in breast imaging, with potential to improve diagnostic accuracy and reduce workload in mammographic breast cancer screening. However, real-world integration of AI into national screening programs remains limited, and little is known about radiologists’ perspectives in Asian settings. This study aimed to explore radiologists’ perceptions of AI adoption in Singapore’s breast screening program, focusing on perceived benefits, barriers, and requirements for safe integration. Methods: We conducted a mixed methods study involving a cross-sectional survey of 17 radiologists with prior experience using AI-assisted mammography, followed by semi-structured interviews with 10 radiologists across all three public healthcare clusters. The survey measured confidence in AI, attitudes toward its diagnostic role, and integration preferences. Interviews were analyzed thematically, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Results: Among survey respondents, 64.7% recommended AI as a companion reader, though only 29.4% rated its performance as comparable to humans. Confidence was highest when AI was validated on local datasets (mean 9.3/10). Interviews highlighted AI’s strengths in routine, fatigue-prone tasks, but skepticism for complex cases. Concerns included false positives, workflow inefficiencies, medico-legal accountability, and long-term costs. Radiologists emphasized the importance of national guidelines, local validation, and clear role definition to build trust. Conclusions: Radiologists support AI as an adjunct to, but not a replacement for, human readers in breast cancer screening. Adoption will require robust regulatory frameworks, seamless workflow integration, transparent validation on local data, and structured user training to ensure safe and effective implementation. Full article

International interests in bioaerosols have gained an increased attention to widen the knowledge pool of their identification, distribution, and quantification. Aeromycota signify a complex and diverse group of fungi dispersed through the atmosphere. Aeromycology is an important field of research due to its important role in human health. Aeromycoflora both indoors and outdoors, are responsible for many allergies and other respiratory diseases. The present review describes the diversity of the aeromycoflora, the techniques used for sampling, identification, and taxonomic classification, and the limitations of the traditional culture-based methods as they fail to detect unculturable species. Furthermore, the spatial and temporal variability in aeromycota complicate consistent monitoring. Both indoor and outdoor environments harbor airborne fungi. The diversity in indoor environments is greatly shaped by the moisture content, building design, and ventilation, which are further taken into consideration. Further, the health impacts of the indoor and outdoor fungi have been discussed and what control measures can be taken to reduce the exposure risks and management strategies that can be adopted. Artificial intelligence (AI) can bring revolution in this field of research and can help in improving detection, monitoring, and classification of airborne fungi. The review finally outlines the emerging role of AI in aeromycology. Full article

With the rapid advancement and widespread adoption of artificial intelligence (AI), AI-powered personalized product recommendation (AI-PPR) has become a core tool for enhancing user experience and driving monetization on short-form video platforms, fundamentally reshaping consumer behavior. While prior research has largely focused on impulse buying intention (I-BI)—purchases triggered by emotional and sensory stimuli—there remains a lack of systematic exploration of spontaneous buying intention (S-BI), which emphasizes rational and cognitively driven decisions formed in unplanned contexts. Addressing this gap, this study integrates the Technology Acceptance Model (TAM) with a perceived evaluation perspective to propose and validate a dual-mediation framework: “AI-PPR → Perceived Usefulness/Perceived Trust → S-BI”. Using a large-scale survey of Generation Z users in mainland China (N = 754), data were analyzed via SPSS 26.0, including reliability and validity tests, regression analysis, and Bootstrap-based mediation analysis. The results indicate that AI-PPR not only has a significant positive direct effect on S-BI but also exerts strong indirect effects through perceived usefulness and perceived trust. Specifically, perceived usefulness accounts for 35.17% and perceived trust for 31.18% of the mediation, jointly constituting 66.35% of the total effect. The findings contribute theoretically by extending the boundary of purchase intention research, differentiating rational S-BI from emotion-driven impulse buying, and enriching the application of TAM in consumption contexts. Practically, the study highlights the importance for short-form video platforms and brand managers to enhance recommendation transparency, interpretability, and trust-building while pursuing algorithmic precision, thereby fostering rational spontaneous buying and achieving a balance between short-term conversions and long-term user value. Full article

Supply chains are a primary contributor to global greenhouse gas (GHG) emissions, rendering their decarbonization an essential dimension of sustainable development. Artificial intelligence (AI) provides a transformative pathway by facilitating proactive emission avoidance through operational efficiency, transparency, and resilience, in contrast to post-emission mitigation approaches such as carbon capture. This study explores the potential of AI to support indirect carbon dioxide removal (CDR) via supply chain decarbonization, adopting a comparative case study methodology. Empirical evidence is drawn from Tunisian agri-food, textile, and port logistics sectors, based on multi-source datasets spanning 6–12 months and covering fleet sizes ranging from 40 to 250,000 units. Methodological robustness was ensured through the use of pre-intervention baselines, statistical imputation for missing data (<5%), and validation against 20% out-of-sample test sets. Results indicate that AI-enabled interventions achieved annual avoided emissions between 500 and 1500 tCO₂ and reduced fuel consumption by 12–15%, with sensitivity analyses incorporating ±8–12% error margins. Among the approaches tested, hybrid models integrating operational and strategic layers demonstrated the most pronounced impact, aligning immediate efficiency gains with long-term systemic decarbonization. Furthermore, AI facilitates renewable energy integration, digital twin applications, and compliance with international sustainability frameworks, notably the Paris Agreement and the United Nations Sustainable Development Goals. Nevertheless, challenges related to data quality, computational demands, limited expertise, and organizational resistance constrain scalability. The findings underscore AI’s dual role as a technological enabler and systemic driver of supply chain decarbonization, advancing its positioning within global environmental sustainability transitions. Full article

This review paper explores the role of artificial intelligence (AI)-driven technologies in transforming road transportation systems within smart cities. Adopting a granular approach to the selected research, it examines the extent to which these technologies contribute to creating intelligent road networks, beginning with their integration into the conceptualization and design of road space. Through a comprehensive review of recently published indexed articles, the study addresses key questions regarding AI’s contribution to smart road systems and their ability to adapt during the transition toward sustainable, technology-enabled urban environments. Additionally, it investigates the boundaries between relevant disciplines, areas of overlap and integration, and the benefits of interdisciplinary dialogue in developing effective AI-driven approaches for the design, implementation, and management of smart urban road systems. The findings aim to guide future research, policymaking, and practical applications, ultimately enhancing urban mobility, quality of life, and user experience within smart city contexts. The scope of this research encompasses a wide range of stakeholders involved in transportation and related fields, fostering a multidisciplinary perspective on sustainable urban mobility. Full article