Search Results (507)

Miao embroidery holds significant cultural, economic, and aesthetic value. However, its transmission faces numerous challenges: a high learning threshold, a lack of interest among younger generations, and low production efficiency. These factors have created obstacles to its sustainable development. In the age of artificial intelligence (AI), generative AI is expected to improve the efficiency of pattern innovation and the adaptability of the embroidery industry. Therefore, this study proposes a Miao embroidery pattern generation and application method based on Stable Diffusion and low-rank adaptation (LoRA) fine-tuning. The process includes image preprocessing, data labeling, model training, pattern generation, and embroidery production. Combining objective indicators with subjective expert review, supplemented by feedback from local artisans, we systematically evaluated five representative Miao embroidery styles, focusing on generation quality and their social and business impact. The results demonstrate that the proposed model outperforms the original diffusion model in terms of pattern quality and style consistency, with optimal results obtained under a LoRA scale of 0.8–1.2 and diffusion steps of 20–40. Generated patterns were parameterized and successfully implemented in digital embroidery. This method uses AI technology to lower the skill threshold for embroidery training. Combined with digital embroidery machines, it reduces production costs, significantly improving productivity and increasing the income of embroiderers. This promotes broader participation in embroidery practice and supports the sustainable inheritance of Miao embroidery. It also provides a replicable technical path for the intelligent generation and sustainable design of intangible cultural heritage (ICH). Full article

The construction sector is particularly prone to financial instability, with insolvencies occurring more frequently among micro- and small-scale firms. The current study explores the application of artificial intelligence (AI) and machine learning (ML) models for predicting insolvency within this sector. The research combined a structured literature review with empirical analysis of construction sector-level insolvency data spanning the recent decade. A critical review of studies highlighted a clear shift from traditional statistical methods to AI/ML-driven approaches, with ensemble learning, neural networks, and hybrid learning models demonstrating superior predictive accuracy and robustness. While current predictive models mostly rely on financial ratio-based inputs, this research complements this foundation by introducing additional sector-specific variables. Empirical analysis reveals persistent patterns of distress, with micro- and small-sized construction businesses accounting for approximately 92% to 96% of insolvency cases each year in the Australian construction sector. Key risk signals such as firm size, cash flow risks, governance breaches and capital adequacy issues were translated into practical features that may enhance the predictive sensitivity of the existing models. The study also emphasises the need for digital self-assessment tools to support micro- and small-scale contractors in evaluating their financial health. By transforming predictive insights into accessible, real-time evaluations, such tools can facilitate early interventions and reduce the risk of insolvency among vulnerable construction firms. The current study combines insights from the review of AI/ML insolvency prediction models with sector-specific feature derivation, potentially providing a foundation for future research and practical adaptation in the construction context. Full article

This study explores how FinTech and artificial intelligence (AI) adoption shape efficiency and financial stability in dual-banking systems. It focuses on 26 listed Islamic and conventional banks across 11 countries in the MENA and Southeast Asia regions between 2020 and 2024. To measure digital adoption, we create a seven-component FinTech Adoption Index. We use fixed-effects regressions to examine its impact on cost efficiency, profitability, solvency stability, and credit risk. This analysis also controls bank size, capitalization, and macroeconomic conditions. The results show a clear adoption gap. Conventional banks consistently score 0.5–0.8 points higher on the FinTech Index compared to Islamic banks. Each additional FinTech component raised operating costs by about 0.8%, but improved profitability slightly by only 0.03%. This suggests that technological integration creates upfront costs before any real efficiency gains are seen. However, the stability benefits are stronger. FinTech adoption increases the Z-score by 3.6 points and lowers the non-performing loan ratio by 0.1%. Islamic banks gain more stability benefits due to their risk-sharing contracts and asset-backed financing structures. Overall, an efficiency–stability trade-off emerges. Conventional banks focus more on profitability, while Islamic banks gain resilience, but face slower efficiency improvements. By combining the Resource-Based View and Financial Stability Theory, this study provides the first multi-country evidence of how governance structures shape digital transformation in dual-banking markets. The findings offer practical guidance for regulators and bank managers around balancing innovation, efficiency, and stability. Full article

The rapid proliferation of Internet of Things (IoT) devices has led to a growing ecosystem of Cloud Platforms designed to manage, process, and analyze IoT data. Selecting the optimal IoT Cloud Platform is a critical decision for businesses and developers, yet it presents a significant challenge due to the diverse range of features, pricing models, and architectural nuances. This manuscript presents a comprehensive, feature-driven review of twelve prominent IoT Cloud Platforms, including AWS IoT Core, IoT on Google Cloud Platform, and Microsoft Azure IoT Hub among others. We meticulously analyze each platform across nine key features: Security, Scalability and Performance, Interoperability, Data Analytics and AI/ML Integration, Edge Computing Support, Pricing Models and Cost-effectiveness, Developer Tools and SDK Support, Compliance and Standards, and Over-The-Air (OTA) Update Capabilities. For each feature, platforms are quantitatively scored (1–10) based on an in-depth assessment of their capabilities and offerings at the time of research. Recognizing the dynamic nature of this domain, we present our findings in a two-dimensional table to provide a clear comparative overview. Furthermore, to empower users in their decision-making process, we introduce a novel, web-based tool for evaluating IoT Cloud Platforms, called the “IoT Cloud Platforms Selector”. This interactive tool allows users to assign personalized weights to each feature, dynamically calculating and displaying weighted scores for each platform, thereby facilitating a tailored selection process. This research provides a valuable resource for researchers, practitioners, and organizations seeking to navigate the complex landscape of IoT Cloud Platforms. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

Adapting contemporary business models to the challenges of implementing new technologies influences the sustainable value of companies. This study examines the disclosure practices of Romanian-listed companies regarding accounting estimates, their correlation with financial performance, ESG scores, and the use of artificial intelligence (AI). Financial data was gathered from annual reports and those regarding the use of AI on companies’ websites. Financial performance was measured through profitability and liquidity indicators. The results of the statistical regressions showed that company size can influence AI disclosure; however, industry is not a strong predictor, and the number of employees does not significantly influence AI disclosure. A positive relationship was found between AI transparency and the current ratio, suggesting that companies disclosing more information about their AI use may have higher current liquidity. Additionally, a statistically significant negative relationship was observed between the AI disclosure score and net profit, indicating that greater AI transparency is associated with lower net income. The results of interaction analysis proved that there may be a relationship between ESG exposure and financial performance when considering AI disclosure. However, this result may be considered controversial in a more conservative analysis, emphasizing the need for a more nuanced and multidimensional approach. Full article

In today’s fast-moving digital economy, service start-ups are reshaping industries; however, they face intense uncertainty, limited resources, and fierce competition. This study introduces an Artificial Intelligence (AI)-powered process modeling framework designed to give these ventures a competitive edge by combining big data analytics, machine learning, and Business Process Model and Notation (BPMN). While past models often overlook the dynamic, human-centered nature of service businesses, this research fills that gap by integrating AI-Driven Ideation, AI-Augmented Content, and AI-Enabled Personalization to fuel innovation, agility, and customer-centricity. Expert insights, gathered through a two-stage fuzzy Delphi method and validated using DEMATEL, reveal how AI can transform start-up processes by offering real-time feedback, predictive risk management, and smart customization. This model does more than optimize operations; it empowers start-ups to thrive in volatile, data-rich environments, improving strategic decision-making and even health and safety governance. By blending cutting-edge AI tools with process innovation, this research contributes a fresh, scalable framework for digital-age entrepreneurship. It opens exciting new pathways for start-up founders, investors, and policymakers looking to harness AI’s full potential in transforming how new ventures operate, compete, and grow. Full article

Emerging technologies present significant challenges for society as a whole. Among these, Artificial Intelligence (AI) stands out for its transformative potential, with the capacity to fundamentally reshape human thought, behavior, and lifestyle. This article seeks to explore the business-oriented perspective on how AI should be approached in Higher Education (HE) in order to serve the commercial objectives of companies. The motivation for this inquiry stems from recurrent criticisms directed at HE institutions, particularly their perceived inertia in adopting innovations, resistance to change, and delayed responsiveness to evolving labor market demands. In this context, the study examines what businesses deem essential for universities to provide in the context of AI familiarity and examines how companies envision future collaboration between the business sector and Higher Education institutions in using AI for business applications. Adopting a qualitative research methodology, this study conducted interviews with 16 middle-management representatives from international corporations operating across diverse industries. The data were analyzed using Gioia’s methodology, which facilitated a structured identification of first-order concepts, second-order themes, and aggregate dimensions. This analytical framework enabled a nuanced understanding of business expectations regarding the role of HE institutions in preparing graduates capable of meeting economic and commercial imperatives under the pressure of AI diffusion. Full article

With the rapid development of artificial intelligence technology, its widespread application in the field of business management has become a significant issue faced by contemporary enterprises. Based on the Technology Acceptance Model, this study explores the impact of AI technology acceptance on organizational decision-making efficiency, performance, and the depth of technology application. It also reveals the driving mechanisms of top management support, perceived usefulness, and perceived ease of use on AI technology adoption through path analysis. To validate the research hypotheses, the study employed structural equation modeling (SEM) based on survey data collected from 420 respondents across various industries. The study found that top management support significantly enhances technology acceptance through perceived variables, while perceived usefulness is the core factor driving technology adoption. Although perceived ease of use has a weaker effect, it is equally important in lowering the psychological barriers during the initial stages of technology adoption. The adoption of AI technology has significantly improved organizational decision efficiency and overall performance, promoting the deep application of technology by optimizing resource allocation and enhancing scientific decision-making capabilities. This study further validates the applicability of the TAM theory in the context of AI technology, expanding its theoretical explanatory power in complex technology-adoption mechanisms. At the same time, the research provides practical guidance for enterprises in the introduction and application of technology, emphasizing that managers need to shape an open and innovative organizational culture at a strategic level and enhance employees’ willingness to accept technology through technical training and value transmission. Future research can incorporate cross-cultural and multi-level analytical frameworks to explore the dynamic adaptation paths of AI technology adoption and its potential risks in sustainable development. Full article

Educational assessment relies on well-constructed test items to measure student learning accurately, yet traditional item development is time-consuming and demands specialized psychometric expertise. Automatic item generation (AIG) offers template-based scalability, and recent large language model (LLM) advances promise to democratize item creation. However, fully automated approaches risk introducing factual errors, bias, and uneven difficulty. To address these challenges, we propose and evaluate a hybrid human-in-the-loop (HITL) framework for AIG that combines psychometric rigor with the linguistic flexibility of LLMs. In a Spring 2025 case study at Franklin University Switzerland, the instructor collaborated with ChatGPT (o4-mini-high) to generate parallel exam variants for two undergraduate business courses: Quantitative Reasoning and Data Mining. The instructor began by defining “radical” and “incidental” parameters to guide the model. Through iterative cycles of prompt, review, and refinement, the instructor validated content accuracy, calibrated difficulty, and mitigated bias. All interactions (including prompt templates, AI outputs, and human edits) were systematically documented, creating a transparent audit trail. Our findings demonstrate that a HITL approach to AIG can produce diverse, psychometrically equivalent exam forms with reduced development time, while preserving item validity and fairness, and potentially reducing cheating. This offers a replicable pathway for harnessing LLMs in educational measurement without sacrificing quality, equity, or accountability. Full article

The pharmaceutical and biotechnology sector is an intricate and rapidly evolving industry encompassing the full lifecycle of drugs, medicines, and clinical devices. Its growth is driven by factors such as the aging population, the rise in chronic diseases, and the increasing focus on personalized medicine. Nevertheless, it also faces significant challenges due to rising costs, increased complexity, and regulatory hurdles. Through a systematic literature review (SLR) as a research method combined with a comprehensive market analysis, this paper explores how several leading early-adopter healthcare companies are increasing their investments in computer-based clinical research information systems (CRISs) to sustain productivity, particularly through the adoption of artificial intelligence (AI) and cloud-native computing. As an extension of this research, a novel 360-degree reference blueprint is proposed for the domain analysis of medical features within AI-powered CRIS applications. This theoretical framework specifically targets clinical trial management systems (CRIS-CTMSs). Additionally, a detailed review is presented of the leading commercial solutions, assessing their portfolios and business maturity, while highlighting major open innovation collaborations with prominent pharmaceutical and biotechnology companies. Full article

Current agentic AI frameworks such as LangGraph and AutoGen simulate autonomy via sequential prompt chaining but lack true multi-agent coordination architectures. These systems conflate semantic reasoning with orchestration, requiring LLMs at every coordination step and limiting scalability. By contrast, TB-CSPN (Topic-Based Communication Space Petri Net) is a hybrid formal architecture that fundamentally separates semantic processing from coordination logic. Unlike traditional Petri net applications, where the entire system state is encoded within the network structure, TB-CSPN uses Petri nets exclusively for coordination workflow modeling, letting communication and interaction between agents drive semantically rich, topic-based representations. At the same time, unlike first-generation agentic frameworks, here LLMs are confined to topic extraction, with business logic coordination implemented by structured token communication. This hybrid architectural separation preserves human strategic oversight (as supervisors) while delegating consultant and worker roles to LLMs and specialized AI agents, avoiding the state-space explosion typical of monolithic formal systems. Our empirical evaluation shows that TB-CSPN achieves 62.5% faster processing, 66.7% fewer LLM API calls, and 167% higher throughput compared to LangGraph-style orchestration, without sacrificing reliability. Scaling experiments with 10–100 agents reveal sub-linear memory growth (10× efficiency improvement), directly contradicting traditional Petri Net scalability concerns through our semantic-coordination-based architectural separation. These performance gains arise from the hybrid design, where coordination patterns remain constant while semantic spaces scale independently. TB-CSPN demonstrates that efficient agentic AI emerges not by over-relying on modern AI components but by embedding them strategically within a hybrid architecture that combines formal coordination guarantees with semantic flexibility. Our implementation and evaluation methodology are openly available, inviting community validation and extension of these principles. Full article

Digital innovation is rapidly transforming the agriculture sector, drawing attention from global development institutions, policymakers, tech firms, and scholars aimed at aligning food systems with international goals like Zero Hunger and the FAO agendas. Small and medium enterprises in agriculture (Agri-SMEs) represent a significant portion of processing and production units but face challenges in digital transformation despite their importance. Technologies such as Artificial Intelligence (AI), blockchain, cloud services, IoT, and mobile platforms offer tools to improve efficiency, access, value creation, and traceability. However, the patterns and applications of these transformations in Agri-SMEs remain fragmented and under-theorized. This paper presents a systematic review of interactions between digital transformation and innovation in Agri-SMEs based on findings from ninety-five peer-reviewed studies. Key themes identified include AI-based decision support, blockchain traceability, cloud platforms, IoT precision agriculture, and mobile technologies for financial integration. The review maps these themes against business model values and highlights barriers like capacity gaps and infrastructure deficiencies that hinder scalable adoption. It concludes with recommendations for future research, policy, and ecosystem coordination to promote the sustainable development of digitally robust Agri-SMEs. Full article

There is minimal research concerning the role of custom-trained artificial intelligence (AI) tools in higher education, with a lack of research concerning the pedagogical qualities of an AI-based personal tutor. To fill this literature gap, we examined how a custom GPT personal tutor shapes key teaching and learning qualities. Using the mixed-methods approach, we aimed to demonstrate preliminary and exploratory empirical evidence concerning the contribution of custom-trained AI tutors to building up students’ competencies. Our research analyzed the subjective assessments of students related to the GPT tutor’s contribution to improving their competencies. Both the qualitative and quantitative empirical results confirmed the positive contribution. In addition, we triangulated the results to evaluate the potential of custom-trained AI chatbots in higher education, focusing on undergraduate business courses. However, the results of this study cannot be generalized to the entire student population of business schools, since the participation in the AI-assisted tutor program was voluntary, attracting only intrinsically motivated students. Full article

This study synthesizes the rapidly expanding evidence on how digital technologies reshape international trade, with a particular focus on small and medium-sized enterprises (SMEs). Guided by two research questions—(RQ1) How do digital tools influence the volume and composition of cross-border trade? and (RQ2) How do these effects vary by countries’ development level and firm size?—we conducted a PRISMA-compliant systematic literature review covering 2010–2024. Searches across eight major databases yielded 1857 records; after duplicate removal, title/abstract screening, full-text assessment, and Mixed Methods Appraisal Tool (MMAT 2018) quality checks, 86 peer-reviewed English-language studies were retained. Findings reveal three dominant technology clusters: (1) e-commerce platforms and cloud services, (2) IoT-enabled supply chain solutions, and (3) emerging AI analytics. E-commerce and cloud adoption consistently raise export intensity—doubling it for digitally mature SMEs—while AI applications are the fastest-growing research strand, particularly in East Asia and Northern Europe. However, benefits are uneven: firms in low-infrastructure settings face higher fixed digital costs, and cybersecurity and regulatory fragmentation remain pervasive obstacles. By integrating trade economics with development and SME internationalization studies, this review offers the first holistic framework that links national digital infrastructure and policy support to firm-level export performance. It shows that the trade-enhancing effects of digitalization are contingent on robust broadband penetration, affordable cloud access, and harmonized data-governance regimes. Policymakers should, therefore, prioritize inclusive digital-readiness programs, while business leaders should invest in complementary capabilities—data analytics, cyber-risk management, and cross-border e-logistics—to fully capture digital trade gains. This balanced perspective advances theory and practice on building resilient, equitable digital trade ecosystems. Full article