06 Príspevky v zborníkoch

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Prehliadanie 06 Príspevky v zborníkoch podľa Predmet "artificial intelligence"

Teraz sa zobrazuje 1 - 4 z 4
  • Obrázok miniatúry
    Položka
    Factors affecting adoption of artificial intelligence in SMEs and its impact on firm's skills needs
    (Niš : Complex system research centre, 2025) Huňady, Ján
    The paper deals with the problem of artificial intelligence (AI) adoption in companies. It aims to empirically examine factors affecting the adoption of AI. It also identifies factors influencing the potential effects of AI adoption on future infirm skills needs. There is currently an absence of research focused on examining this topic based on empirical data. The research is based on the secondary data from Flash Eurobarometer 537 (2023). More than 19,000 companies from 27 EU countries and 9 non-EU countries have been included in the sample. By examining these data, substantial systematic differences were found between companies with different characteristics. Factors affecting AI adoption and consequences on skills needed have been identified by logistic regression. The results suggest that larger companies with older employees located in cities, as well as those hiring adequate skills without any problems, are often adopting AI. Similar characteristics are also typical for companies that reported a significant effect of AI on their skill needs. However, in this case, the effect of community size and difficulties with hiring skills are not significant. Significant differences were also identified among industries and types of specific skills that a company lacks
  • Obrázok miniatúry
    Položka
    HR digitisation before and during COVID-19 in Visegrad group countries
    (Uniwersytet Szczeciński : Szczecin, 2025) Misiak-Kwit, Sandra; Smerek, Lukáš; Szabó-Szentgróti, Gábor; Marková, Helena
    Purpose: This paper aims to provide new insights into the digitisation of human resources in the Visegrad Group. The authors examined the evaluation of HR digitisation before and during the COVID-19 pandemic. Need for the study: The digitisation of human resource management in the Visegrad Group countries is an emerging phenomenon that influences how organisations in these regions manage their human resources. Methodology: The survey included the following sample sizes: 500 respondents in Poland, 832 respondents in the Czech Republic, 384 respondents in Slovakia, and 377 respondents in Hungary. The Shapiro-Wilk normality test, F-test for equal variance, and paired t-test were utilised for the investigation. Additionally, the authors conducted a one-way ANOVA to compare the mean digitisation scores among Visegrad Group countries before and during COVID-19. Findings: The data presented in the paper show that HR digitisation is consistently ranked as one of the less challenging HR activities in the Visegrad Group countries, both before and during the COVID-19 pandemic. The results indicate that while country-specific differences in HR digitisation challenges were significant both before and during COVID-19, the pandemic exacerbated these differences. Hungary consistently faced the greatest challenges, while Poland encountered the fewest. The pandemic heightened the overall difficulty of HR digitisation, as evidenced by higher mean scores and greater variability. Practical Implications: The paper emphasises the urgent need for targeted strategies to promote digital transformation, especially in countries facing significant challenges, during crises and beyond.
  • Obrázok miniatúry
    Položka
    Insurance as a digital ecosystem: exploring new horizons of digitalization, smart business, and global competitiveness
    (Technická Univerzita v Liberci, 2025) Benetti, Karina; Bobojonov, Azizjon; Izáková, Katarína; Eshov, Mansur
    The insurance industry is undergoing a profound transformation driven by the integration of artificial intelligence (AI), big data analytics, the Internet of Things (IoT), blockchain, and cybersecurity frameworks. These innovations are not incremental improvements but are reconfiguring the insurance value chain into a digital ecosystem—an interconnected environment characterized by real-time data flows, algorithmic decision-making, and platform-based business models. Within this ecosystem, risk management is shifting from retrospective compensation towards predictive and preventive strategies, with telematics, wearables, and smart sensors enabling usage-based insurance. This paper examines the systemic impact of digitalization by analyzing the synergies among emerging technologies and their ethical, regulatory, and operational implications. AI enhances underwriting, fraud detection, and claims automation but raises concerns about bias and transparency. Big data expands predictive modelling and personalization while intensifying privacy and governance challenges. The study argues that digital transformation requires moving beyond isolated technology adoption towards the design of resilient and inclusive insurance ecosystems. By framing digitalization as a structural and societal paradigm shift, this paper contributes a comprehensive perspective for academics, practitioners, and policymakers seeking to understand and guide the future of insurance.
  • Obrázok miniatúry
    Položka
    Utilization of AI-driven manufacturing technologies: an empirical study
    (TRAUNER Verlag : Linz, 2025) Závadský, Ján; Závadská, Zuzana
    The article aims to identify the current and anticipated use of AI-driven manufacturing technologies in Slovak manufacturing companies. Building on a 2017 study by Závadský& Závadská, the research revisits and expands the original sample to 115 companies. The study examines the adoption and future expectations of 10 selected AI technologies through a structured survey of quality and production managers. Key findings show that 3D Printing, Predictive Maintenance Systems, Generative Design, and Supply Chain Optimization Systems are the most widely used. Digital Twins, Robotic Process Automation, and Collaborative Robots are the most anticipated. Predictive Maintenance, though widely used, has limited future growth potential. The data reveal the current state and trajectory of AI integration in manufacturing. Statistical tests confirm the sample’s representativeness across industries. These partial results contribute to understanding AI's practical implementation and expected evolution in industrial settings.