The rapid integration of artificial intelligence (AI) into consumer markets has transformed the brand positioning and consumer evaluation of AI-enabled smart products. Although technology acceptance research has established perceived usefulness as a central predictor of adoption, it has not sufficiently explained how AI-powered brand positioning and digital customer value shape adoption intention under conditions of digital trust. This study extends the technology acceptance model by positioning AI-powered brand positioning and digital customer value as external AI-marketing antecedents of perceived technology usefulness while conceptualizing digital trust as a boundary condition that determines when these antecedents translate into smart product adoption intention. The proposed model was empirically tested using structural equation modelling with bootstrapping and latent moderated structural equation modelling on survey data collected from 464 consumers of AI-enabled smart home devices in large Chinese urban centres. The results indicate that AI-powered brand positioning and digital customer value have significant positive effects on adoption intention and that these relationships are partially mediated by perceived technology usefulness. The findings further show that digital trust strengthens the effects of AI-powered brand positioning and digital customer value on adoption intention, with stronger effects among high-trust consumers and weaker effects among low-trust consumers. These findings extend the technology acceptance model by showing that AI-enabled marketing capabilities and digital value perceptions serve as external antecedents of perceived technology usefulness, whereas digital trust functions as a boundary condition that determines when these antecedents are more likely to lead to the intention to adopt smart products. Theoretically, this study shows that AI-enabled smart product adoption cannot be explained by usefulness evaluations alone; rather, adoption emerges from the interaction between AI-driven brand strategy, digital value perception, and trust in algorithmic systems. In practice, the analysis reveals that investments in AI-powered brand positioning and digital customer value are unlikely to translate into adoption intentions for smart products without a foundation of digital trust, and transparent data policies and the responsible use of artificial intelligence can facilitate further research. The cross-sectional design and focus on urban Chinese buyers limit the generalizability of the results; future studies should investigate longitudinal effects and cross-cultural variations in AI-driven adoption behaviours.
The circular economy concept has responded to the growing demand for raw materials. Its goal is to ensure that all product and material flows can be reused after their initial use, becoming resources for new products and services. This concept requires the participation of all stakeholders, including consumers, designers, materials experts, developers, companies, investors, nonprofit organisations, academics, and policy makers. In this context, consumers play a particularly important role, as their decisions about purchasing, using, and disposing of products strongly influence how successfully circular principles are implemented in practice. This model is becoming increasingly important as a response to the growing pressure on natural resources and the environment and as a tool for more sustainable economic development. This study contributes to the understanding of the circular economy in Slovakia. It systematically connects environmental attitudes, consumer behaviour, and sociodemographic characteristics, especially gender, thereby filling an important research gap in Central and Eastern Europe. This study seeks to investigate customer engagement in circular practices in Slovakia and to deepen the understanding of gender differences. Using the Perceived Characteristics of Innovating (PCI) framework, nearly 600 respondents (338 females, 242 males) were surveyed in 2024 to measure four dimensions: awareness of circular economy principles, ability to identify environmentally friendly products, willingness to participate in pro-circular social activities, and preference for circular-designed products. Data were analysed with Levene’s test for variance equality and independent t tests (or chi-square tests where appropriate). The results confirmed that there were no significant sex differences in awareness of circular economy principles or product identification ability. However, compared with males, females demonstrated significantly greater willingness to engage in social activities supporting circularity and a greater propensity to purchase eco-friendly products. These findings suggest that although females and males share similar levels of awareness and product recognition, targeted interventions such as gender-sensitive educational campaigns and marketing strategies are needed to increase female consumers’ social engagement and eco-friendly purchasing behaviour in Slovakia.
Innovation in healthcare is frequently associated with large organisations that possess substantial financial resources, advanced research infrastructure, and long-term development capacity. Therefore, the concept of innovation might be linked to large-scale technological breakthroughs or systemic transformations initiated by major institutional actors. Such a perspective, however, constrains the understanding of innovation by overlooking the contributions of smaller organisations and limiting the inclusivity and diversity of intellectual capital within the healthcare sector. The purpose of this research was to construct a more inclusive definition of innovation in healthcare. Expanding the boundaries of the concept should encourage wider participation in innovation creation, thus increasing the sustainability of healthcare systems. The aim was achieved by applying quantitative and qualitative research methods. The former method was the distance reading method applied to the three corpora specifically collected for this study. The qualitative method was applied to interviews, which were specifically conducted for this study at a medium-sized healthcare clinic in Latvia, to complement and validate the findings derived from the corpus analysis. The obtained results did not provide strict criteria for the formulation of the concept of innovation; however, they highlighted the association between innovation and improved efficiency and productivity, as well as the importance of incorporating user perspectives when assessing its impact. These findings support a broader and more inclusive understanding of innovation in healthcare and justify the democratisation of the concept by recognising diverse stakeholder perspectives in its definition and evaluation. The obtained research findings also pointed to a blurred boundary between innovation and improvement, which is why further research should focus on identifying specific criteria that separate innovation from upgrades. With respect to the practical implications of this study, healthcare providers are suggested to adopt a broader perspective on the evaluation of innovation while considering incremental and context-specific aspects. The value of this research pertains to broadening the perspective of the conceptualisation of innovation for the purposes of inclusivity and the democratisation of innovation development.
The accelerating digital transformation of the insurance sector intensified competitive pressure, and the expanding use of data science technologies have substantially reshaped marketing strategy formation, creating new opportunities for more precise customer segmentation, predictive analytics, and strategic optimization in insurance companies. The aim of this paper is to examine how data science techniques can be operationalized to support marketing strategy formation in the Ukrainian insurance market, which is characterized by high uncertainty and structural transformation. This study addresses three research questions: which customer attributes are the most significant determinants of insurance product selection in the Ukrainian insurance market; to what extent can decision tree models provide accurate and interpretable segmentation for insurance marketing purposes; and how can the results of data science models be translated into actionable insights for marketing strategy implementation by insurers and practitioners? Using anonymized customer relationship management (CRM) data from a top-10 Ukrainian insurer (N = 14,000 clients), a decision tree classification model was developed to identify key determinants of insurance product choice. Model performance was evaluated using accuracy metrics and rule support. A qualitative description was employed to support audience interpretation. The results indicate that customer loyalty (measured by the number of active contracts), geographic location, and age are the most significant predictors of insurance product selection, with an overall classification accuracy of 93.5%. The extracted decision rules provide transparent and interpretable insights that can be directly translated into targeted marketing actions. The findings demonstrate how interpretable machine learning models can support data-driven targeting, improve marketing return on investment (ROI), and enhance personalization in insurance services. The study highlights key areas of data science application in insurance marketing, including customer segmentation, targeted marketing, and campaign optimization. This study contributes empirical evidence from an under researched, high-uncertainty market and proposes a structured, interpretable analytics approach based on classical machine learning for insurance marketing strategy development. It bridges the gap between analytical modelling and practical marketing strategy, offering insights that are directly applicable to insurance practitioners
This article examines the possibility of forecasting the purchasing activity of customers of transport and logistics companies on the basis of the analytical use of customer relationship management (CRM) system data in the context of digital transformation and the data-driven supply chain management paradigm. The relevance of the study is driven by increasing competition in the logistics services market, the growing complexity of supply chains, the fragmentation of customer data, and the insufficient utilization of the analytical potential of CRM systems among domestic B2B logistics companies. The purpose of the study was to develop and empirically test adaptive forecasting models, identify key factors influencing purchasing activity, and assess the economic efficiency of implementing a CRM system to improve the effectiveness of customer interactions. The methodology employs econometric analysis and adaptive time series forecasting models, including Brown’s, Holt’s, and Holt-Winters’ methods. The empirical basis of the study consists of CRM data from a transport and logistics company for the period 2016–2024, integrated with operational performance indicators. The findings indicate that customer purchasing activity is influenced primarily by company performance and logistics service promotion expenditures, highlighting their strategic significance in demand forecasting. The economic assessment of implementing the Uspacy CRM system integrated with Stream Telecom IP telephony demonstrates its potential to improve operational efficiency, customer interaction management, and financial performance. This integration improves the coordination of sales, communication, and analytical processes, supporting more effective strategic decision-making. This study contributes to the advancement of CRM-based predictive analytics in transport and logistics by demonstrating its applicability for customer behaviour forecasting and its managerial relevance for data-driven supply chain optimization.
Ensuring sustainable development under conditions of rising geopolitical and energy-related risks requires a strategic reconsideration of how national energy systems are developed and governed. For Ukraine, which faces structural vulnerabilities and external shocks, the development of a modern and resilient energy infrastructure management model is crucial. The purpose of this study is to provide an integrated evaluation of Ukraine’s energy infrastructure development from the perspective of energy resilience and to identify the managerial and innovation-oriented principles that shape the country’s ongoing energy transformation. The analysis is based on statistical data from the World Bank and the State Statistics Service of Ukraine over twenty years. Descriptive statistics are used to examine long-term trends in renewable energy development, energy dependence, carbon emissions, and energy intensity. Correlation analysis is applied to reveal structural interrelations within the energy system. A methodological approach for assessing integrated energy resilience is developed by incorporating weighting coefficients for key indicators. Forecasting of temporal trends and regression analysis are used to identify the main determinants of system behaviour. Strategic conditions for energy infrastructure development are systematically analysed through political, economic, social, and technological assessments. The findings demonstrate that the expansion of renewable energy sources has become a core managerial mechanism for strengthening the stability of Ukraine’s energy system. The integrated sustainability indicator shows a gradual upwards trajectory, with a pronounced improvement following the rapid increase in renewable energy capacity during the most recent phase of sectoral modernisation. The regression results confirm that an increasing share of alternative and nuclear energy, together with higher consumption of energy from renewable sources, is associated with lower energy intensity and reduced carbon emissions. These relationships indicate that managerial decisions in investment planning, regulatory policy, and technological upgrading directly influence the behavioural dynamics and structural adaptation of the national energy system. The analysis reveals that the resilience of Ukraine’s energy infrastructure is shaped primarily by managerial approaches grounded in innovation. The modernisation of regulatory mechanisms, the creation of incentives for clean energy investment, the strengthening of international technical partnerships, and the introduction of digital monitoring tools represents key drivers of systemic transformation. The combination of strategic analysis and quantitative assessment enabled the identification of critical leverage points through which managerial interventions can accelerate the transition toward a sustainable energy model. The innovation-oriented perspective highlights the importance of integrating technological solutions, strategic foresight, and adaptive governance in shaping the long-term resilience of the energy system.
Drone technology is being adopted in delivery services, but the behavioural insights of consumers have not been fully researched. Drone delivery companies should determine the reasons behind people utilizing their services. Consumers' early perceptions of emerging technologies such as drone delivery services are essential to its successful introduction. This paper develops the unified theory of acceptance and use of technology, which involves the addition of the meaning of personal innovativeness as a construct in the study of the determinants of the behavioural intention and actual use of drone delivery services in Jordan. The current literature addresses the subject of logistics and risks, but there is no insight into the preferences and factors of adoption among users. To fill the gap, this study considered the behavioural intention and actual application of drones. Snowball sampling was used to select the participants to be included in the study, from which a sample size of 343 Jordanian citizens was obtained. Data collected through an online survey were assessed for reliability and normality tests. The results of the study have shown that the expectancy of effort, facilitating conditions, habits, and personal innovativeness play significant roles in the behavioural intention, which mediates the actual utilization of drone delivery services. Contrary to expectations, performance expectancy, social influence and price value were not significantly involved in this scenario. This paper highlights the importance of managing perceived danger, improving infrastructure, and developing easy-to-use interfaces to increase the adoption of drone delivery services. The results have implications for both logistics providers and policy makers who would like to expedite the implementation and adoption of drone delivery services in new markets. As a result, drone technology and IT innovations should be adopted by the delivery service market of Jordan to increase efficiency and promote the use of drones. Moreover, they are also expected to modernize the appropriate regulation and secure the system as much as possible to reduce risks, increase trust and make customers adopt the services.
The transition toward a low-carbon and environmentally sustainable economy has become a strategic priority in the European Union. This shift places increasing pressure on firms to adopt green innovations. Small and medium-sized enterprises (SMEs) play a crucial role in economic development and industrial production in Central Europe; however, their engagement in green innovation activities remains limited. Despite growing regulatory demands and societal expectations, SMEs often face structural, financial, and organizational constraints that hinder the effective implementation of environmentally sustainable practices. Existing empirical evidence suggests that these barriers are not uniform and may vary depending on firm size and organizational characteristics. This study aims to fill this gap by systematically examining the key barriers to the adoption of green innovations among manufacturing SMEs in Central Europe, with a particular focus on differences related to employment size. The empirical analysis is based on primary quantitative data collected through a structured questionnaire administered via computer-assisted telephone interviewing (CATI). The final sample consists of 184 senior managers from manufacturing SMEs operating in Central Europe, classified as micro, small, and medium-sized enterprises according to OECD criteria. The respondents assessed fourteen potential barriers to the adoption of green innovations using an ordinal Likert-type scale. The reliability of the research instrument was confirmed, with a Cronbach's alpha of 0.82. Given the nonnormal distribution of the data, nonparametric statistical methods were applied. The Kruskal-Wallis test was used to identify differences across firm size categories, whereas the Dwass-Steel-Critchlow-Fligner post hoc test was applied to examine pairwise group differences. The findings reveal statistically significant differences in perceived barriers to the adoption of green innovations among SMEs of different sizes. Micro and small enterprises report greater challenges related to several factors. These include a lack of skilled personnel, limited technological capabilities, weak cooperation with research institutions, and market dominance by established firms. In contrast, medium-sized enterprises more frequently perceive the low prioritization of energy consumption reduction as a relevant obstacle. Financial constraints, regulatory complexity, and insufficient access to knowledge and external expertise emerge as the most critical barriers, disproportionately affecting smaller firms. The results highlight the heterogeneous nature of green innovation barriers within the SME sector and highlight the need for size-specific policy interventions. Targeted financial support schemes, regulatory simplification, and stronger linkages between SMEs, research institutions, and policymakers are essential to accelerate the diffusion of green innovations. By providing empirical evidence from Central European manufacturing SMEs, this study contributes to the literature on sustainable innovation and offers practical implications for the design of more effective green transition policies.
This study examines the relationship between management students' career direction and sustainability attitudes, focusing on the mediating function of perceived importance. Understanding how sustainability-related values transfer into career-relevant outcomes is crucial considering the growing pressure on higher education institutions to incorporate sustainability into curricula and professional training. Based on the Theory of Planned Behaviour (TPB) and the Value-Belief-Norm (VBN) framework, the study creates a conceptual model that incorporates attitudinal, perceptual, and career-related factors. It specifically looks at how students' perceptions of sustainability's significance are influenced by positive attitudes toward it, and how these perceptions in turn affect how sustainability information is perceived to have a professional impact. Using survey data gathered from 257 students participating in management programs at a Romanian higher education institution, the model is empirically verified using partial least squares structural equation modelling (PLS-SEM). The findings show that whereas perceptions strongly determine the perceived professional impact, attitudes toward sustainability have a considerable positive effect on views of its importance. Furthermore, the results validate a full mediation effect, suggesting that attitudes function through cognitive assessments of relevance and importance rather than directly influencing perceived career success. These findings demonstrate how important perception is as a cognitive process that converts abstract sustainability values into expectations that have professional significance. The study also adds to the body of literature from a theoretical standpoint by combining cognitive and attitudinal aspects into a cohesive explanatory model of professional orientation in sustainability education. From a practical standpoint, the results indicate that higher education institutions should actively include sustainability into experiential learning, curriculum design, and career development strategies, going beyond awareness-based approaches. Increasing students' perceptions of sustainability's professional significance may boost their involvement and aid in the training of future managers who can handle challenging sustainability issues.
This study examines motivational and demotivational factors among employees in public administration. At present, public services face increasing pressure to increase their efficiency. These services must adapt to modern management trends and respond to the changing needs of society. These developments create new challenges in the area of employee motivation. The public sector workforce consists of multiple generations with different values, expectations, and work habits. These factors affect not only employees' job satisfaction but also the overall effectiveness of organizations. The study is based on a sample of 242 public administration employees. Data were collected using a structured questionnaire designed to examine generational differences in the perceptions of motivational and demotivational factors. Statistical testing of data related to individual motivational and demotivational factors revealed that some factors demonstrated statistically significant differences. For Generation X, the results indicate the importance of factors such as job stability and regular salary with associated benefits. Political pressures and top-down interventions also emerged as highly significant factors. For Generation Y, statistically significant factors included the meaningfulness of work and work-life balance. Key demotivational factors include the unequal distribution of workload. For the youngest Generation Z, meaningful work and work-life balance were also identified as key motivating factors. Among the demotivational factors, insufficient recognition and feedback, as well as a lack of modern technologies, predominated. The examination of motivational and demotivational factors among employees in public administration therefore represents an important step toward understanding how to increase their engagement, satisfaction, and performance. The results support the development of targeted motivational strategies and a working environment that fosters loyalty, professional the more effective functioning of public services and to strengthening public trust.
Under conditions of limited vineyard areas, market globalization, climate change, and rising resource costs, the optimization of grape cultivation expenditures becomes a critical determinant of profitability and sustainable development in the wine industry through the implementation of managerial innovation. The purpose of this study is to develop an innovative analytical model for assessing the volume of wine production on the basis of accounting data and to analyse the financial and technological drivers within the cost chain. The research examines the multistage and climate-sensitive nature of grape cultivation, emphasizing the importance of structured accounting information for monitoring cost formation and production efficiency across technological stages. It proposes an integrated analytical approach that links financial and technological data within a unified production chain, enabling the identification of cost behaviour patterns, risk zones, and performance dependencies. The proposed innovative cost chain analytical model enables a comprehensive evaluation of performance, considering the specific features of production processes, cost structure, degree of raw material processing, and investment activity of enterprises, thereby enhancing the objectivity and relevance of managerial decision-making in the formation of the raw material base, production, and commercialization of wine products. On the basis of the constructed correlation and regression equations, a quantitative assessment was conducted to determine the effects of material intensity, labour costs, grape production cost, capital investments, and yield on the output volume of grape-growing enterprises. The proposed model establishes an analytical framework for improving the cost management, budgeting, production planning, profitability enhancement, and strategic resilience of wine enterprises. The conducted analysis facilitates advanced forecasting of enterprise performance and strengthens the validity of managerial decisions related to production planning, market realization, and resource optimization within the wine industry. The results contribute to improving transparency, analytical depth, and adaptability of management systems in wine enterprises under conditions of uncertainty.
This paper highlights the arguments and counterarguments in the scientific discussion on adopting metaverse technology in the evolving landscape of digital marketing. The main purpose of the research is to identify the factors that influence the intention of digital marketing executives in Bangladesh to adopt the metaverse in their marketing strategies. Systematization of the literature and approaches to solving this problem indicates that, while various marketing paradigms have evolved from traditional to AI-based marketing, the metaverse represents a novel and immersive frontier that blends digital and physical experiences. The study used a descriptive research design and a structured questionnaire with a 7-point Likert scale. A snowball sampling technique was used to identify 400 digital marketing executives across Bangladesh, of whom 211 provided valid responses, which were analysed using structural equation modelling in SmartPLS 4.0. The object of research is digital marketing executives in Bangladesh, because they represent the forefront of adopting and implementing new marketing technologies within organizations. Results showed that awareness of the metaverse significantly enhances perceived ease of use and perceived usefulness, which, in turn, positively influence attitude towards the metaverse. Furthermore, industry influence emerged as the most impactful factor shaping attitudes, while organizational culture did not demonstrate statistical significance. Additionally, perceived trust and security were found to negatively moderate the relationship between attitude and behavioural intention to use the metaverse. The research empirically confirms and theoretically proves that both individual and external organizational factors significantly drive metaverse adoption in marketing. The results of the research can be useful for marketing practitioners seeking to integrate immersive technologies into their strategies, as well as for academicians aiming to develop further the theoretical foundations of digital marketing transformation in developing countries.
Neuromanagement is an interdisciplinary field that integrates neuroscience, management, and psychology, demonstrating significant growth potential in developing human capital and enhancing organizational effectiveness. In this study, a dual-database perspective is provided on how neuromanagement intersects with time-dependent modelling and computational approaches. The study maps the connections between neuromanagement and time series modelling, focusing on the integration of neurodata with sequential and predictive approaches. Publications indexed in Web of Science and Scopus are analysed using VOSviewer (keyword co-occurrence, co-authorship, and citation links) and are supplemented with WordSift text visualization, creating a "crosswalk" between database categories and classes of time series models. To strengthen methodological interpretation, disciplinary classifications are linked with families of temporal models used in neuroscience, economics, and management. A significant acceleration has been observed in publication activity over recent years, characterized by three core pillars (neuroscience, computer science/AI, and economics/business) and a geographical dominance of the USA and Europe, accompanied by increasing involvement from Asia. WoS and Scopus consistently identify the axes of neuromarketing, neuroeconomics, and decision-making, with Scopus additionally emphasizing the technical "toolbox" layer (biosignal processing, deep learning, brain-computer interfaces) and clinical-demographic granularity. These findings illustrate the progressive convergence between cognitive, computational, and managerial paradigms. On this basis, an operationalization framework is proposed for the management domains of conflict/cooperation, motivation/reward, uncertainty/risk, and perception/attention, which connects neurodata with organizational performance indicators. The framework outlines methodological pathways for empirical pilots, facilitating the integration of neurophysiological signals with sequential modelling techniques in managerial practice. By providing a comprehensive conceptual and methodological map across two major indexing databases, the study supports the evolution of neuromanagement as an emerging hybrid discipline and offers guidance for future applications of time-dependent models in organizational decision-making.
The evaluation of healthcare system effectiveness constitutes an integral component of healthcare management, yet existing assessment practices differ substantially in their capacity to support management-oriented interpretation and system adaptation. This study examines the performance of healthcare systems in European countries from a healthcare management perspective, focusing on the adaptive interaction between institutional and financial capacities. Effectiveness is conceptualized as the ability of healthcare systems to transform institutional and financial inputs into population health outcomes, operationalized through measures such as life expectancy at birth. The analysis employs a multimethod evaluation framework that combines data envelopment analysis and stochastic frontier analysis, enabling the joint consideration of both deterministic efficiency and stochastic variability. A comparative assessment is conducted for 34 European countries over the period 2000-2023. On the basis of the estimated efficiency scores, countries are classified according to their distinct healthcare system performance profiles. The results indicate that system performance cannot be sufficiently explained by financial capacity or institutional quality considered in isolation. Instead, effectiveness emerges from their combined and adaptive configuration, extending existing interpretations of healthcare system performance beyond linear input-output relationships. The findings reveal differentiated managerial implications across country groups. Healthcare systems that consistently exhibit high efficiency are characterized by stable institutional arrangements and sufficient financial capacity, enabling the scaling of existing management mechanisms and innovative practices. Systems demonstrating persistently low efficiency require strengthening managerial capacity, improving institutional coordination, and enhancing the transparency of financial flows through systematic performance monitoring. Countries displaying significant discrepancies between efficiency estimates point to the need for strategic management decisions aimed at mitigating the effects of stochastic variability and external shocks. The proposed multimethod assessment framework enhances the managerial relevance of healthcare system evaluation by supporting adaptive decision-making related to institutional and financial capacity. The results contribute to the development of innovative healthcare management approaches and provide an analytical basis for the comparative evaluation and strategic planning of healthcare systems in the European context.
This study aims to explore the potential of industrial symbiosis (IS) to advance circular material use, with a particular focus on the valorisation of biomass ash in the construction industry. Positioned at the intersection of sustainability and innovation management, IS is analysed as both an environmental strategy and a systemic innovation process that enables crosssectoral resource efficiency. Despite the significant quantities of biomass combustion residues generated across Europe, most are still landfilled, even though their chemical composition makes them suitable as supplementary cementitious materials. To address this gap, this research combines bibliometric analysis, an extensive literature review, and a conceptual case study. The bibliometric analysis of 403 Scopus-indexed publications reveals that while biomass ash research has grown substantially since 2006, its integration into IS networks remains limited. The literature review further identifies dominant research themes, i.e., cementitious applications, combustion processes, and environmental impacts, while highlighting the underrepresentation of governance, business models, and IS frameworks in biomass ash studies. A conceptual case study was conducted in Liepaja, Latvia, a regional hub for the renewable energy and construction industries. The study assesses spatial proximity, interorganizational coordination, and digital matchmaking as enablers of resource exchange. From a management perspective, this research underscores the importance of stakeholder engagement, adaptive governance, and innovation ecosystems in supporting the transition from waste to value. The originality of this study is reflected in its integration of fragmented biomass ash research with IS theory and its contribution to innovation management by framing IS as a collaborative and systemic innovation. The findings demonstrate that IS can drive industrial transformation by aligning environmental objectives with business strategies and regional sustainable development goals.
This study investigates the relationship between the linguistic and sentimental characteristics of TikTok hooks, which are defined as the brief opening seconds of short-form video content, and subsequent digital engagement levels. The research aims to determine whether the frequency and contextual use of specific lexical features, as well as the emotional tone present in hooks, are associated with key interaction metrics on TikTok, such as views, likes, and comments. To achieve this objective, a dataset of 4983 hooks was compiled by scraping publicly available TikTok content published between January 2020 and December 2022. Audio-to-text transcription facilitated the extraction of linguistic information, which was subsequently analysed via three sentiment analysis methods: Sentimentr, AFINN, and Syuzhet. These methods were employed to capture contextual polarity, affective intensity, and emotional variation. Statistical analyses included Spearman's correlation tests to assess the relationship between lexical frequencies and engagement metrics, as well as Wilcoxon's nonparametric tests to compare sentiment profiles between hooks with above-and below-median engagement levels. The findings indicate no statistically significant correlation between the selected lexical features and any of the interaction metrics, suggesting that simple word frequency is not a reliable predictor of user engagement. However, significant differences were observed in sentiment profiles, particularly for the number of views, implying that emotionally charged hooks may be more effective at capturing initial audience attention and may benefit from early algorithmic amplification. These results are consistent with theoretical perspectives from the attention economy, which emphasize the role of emotional salience in shaping user behaviour during the initial moments of content exposure. Several methodological limitations are acknowledged, including potential inaccuracies in audio-to-text transcription, the exclusion of multimodal features such as visual and audio cues, and reliance on lexicon-based sentiment tools that may not fully capture nuanced emotional expression. Future research should incorporate multimodal analysis, semantic modelling, and experimental designs to more comprehensively examine how linguistic, visual, auditory, and algorithmic factors collectively influence engagement patterns on short-form video platforms.
The main goal of this research is to develop a fuzzy dynamic model for assessing the level of image of a destination for cultural tourism on the basis of an intellectual analysis of knowledge, the experience of participants in the tourist movement, and the ability to forecast cultural tourism data, using examples of the countries of the Visegrad Group (Czech Republic, Hungary, Poland, Slovakia). To formalize a fuzzy dynamic model, the mathematical apparatus of expert evaluation and intellectual analysis of knowledge, theory of fuzzy sets and fuzzy logic, linear regression, and multicriteria evaluation of alternatives are used. For the first time, a multicriteria two-stage model for evaluating the image of a destination for cultural tourism has been developed. At the first level, cultural tourism data are predicted; at the second level, the problem of multicriteria selection of the destination (region) is solved, considering the wishes of the decision maker regarding cultural aspects related to tourism. For the first time, an information model of destination evaluation and choice in the context of cultural tourism has been developed, which reveals the uncertainty of incoming expert judgments regarding the expected and real experience, impressions, and satisfaction with the destination of cultural tourism, moving from the opinions of the individual to the group opinion, which is represented by a quantitative normalized assessment. A fuzzy dynamic model for evaluating the image of a destination for cultural tourism was verified and tested on real data from 2,343 respondents in the countries of the Visegrad Group. The proposed model offers a flexible analytical framework that captures both subjective tourist perceptions and objective temporal trends, which significantly increases the accuracy of destination image assessment. The results may serve as a practical tool for policymakers and tourism managers seeking to improve strategic planning, enhance regional competitiveness, and strengthen cultural tourism development. Future studies should apply advanced predictive methods, extend the model to other tourism sectors and geographic contexts, and incorporate digital behavioural data (e.g., online reviews and mobility records). Longitudinal data collection would further improve the dynamic tracking of the destination image.
EU Green Deal and related regulations require the textile and fashion industries to adopt sustainable business practices and innovative solutions. The European Commission's Circular Economy Action Plan identifies the textile and fashion sector as one of seven key industries necessitating strategic sustainability transitions aligned with the Sustainable Development Goals (SDGs) and broader global challenges. In response, companies in this sector are increasingly implementing measures related to sustainable production and consumption, reducing water usage, ensuring fair labourpractices, adopting sustainable managerial approaches, and fostering sustainability-oriented innovations within their supply chains. Despite growing global attention to sustainability, research on sustainability-oriented innovation (SOI) and circular supply chains in this industry remains fragmented. Therefore, this study aims to analyse the determinants of SOI in the textile and fashion supply chains. A synthetic literature review was employed to examine the key factors influencing the implementation of the SOI. The analysis identified three main categories of factors affecting SOI in supply chains: internal organizational factors, market-and consumer-driven elements, and regulatory and policy drivers. These determinants collectively support companies in pursuing SOI as a pathway to sustainable value creation. The study is grounded in the development of a conceptual model and contributes to both sustainability research and the application of the triple bottom lineapproach. This research offers two main contributions: first, it provides a comprehensive understanding of sustainability-oriented innovations within supply chains; second, it proposes a structured framework to examine the drivers of SOI in the textile and fashion industry. The findings serve as afoundation for future empirical studies and offer insights for stakeholders interested in advancing sustainable supply chain innovation
This article summarises the arguments and counterarguments of scientific discussion on the adoption of green innovations by tourism enterprises in V4 countries, with a specific focus on the simultaneous influence of regulatory stimuli, product demand, and enterprise size. The primary objective of thisresearch is to identify and examine the determinants of the implementation ofenvironmental measures in the tourism sector, as well as to quantify their interrelationships. The systematisation of literature sources and approaches to address thisissue indicates that most existing studies have a national focus and lack comparative cross-country research that integrates policy, market, and business factors. The relevance of this scientific task lies in the fact that transitional economies often fail to recognisethe synergistic effect of these factors, which may hinder the successful adoption of green tourism. The research follows a logical sequence: theoretical background and literature review, clarification of methodology, presentation of empirical results, comparative explanation, and formulation of conclusions and policy implications. The methodological framework combines quantitative survey techniques with multiple regression analysis applied to tourism enterprise data within the V4 region. The object of the study comprises tourism enterprises in V4 countries, which operate under differing policy and market conditions yet face similar sustainability challenges. The empirical findings confirm that legislative incentives and consumer demand are the principal drivers of environmental measure implementation, whereasenterprise size serves as a significant moderating factor. These results empirically support and theoretically reinforce the necessity of complementary policy and market measures to achieve sustainability in tourism. The study extends existing knowledge by demonstrating the interplay between regulatory frameworks, market demand, and business characteristics in shaping sustainability practices. The findings are of practical relevance for policymakers, tourism organisations, and entrepreneurs in designing targeted policy instruments, optimising incentive schemes, and removing administrative barriers. Furthermore, the study highlights the need for further longitudinal and cross-regional research to monitor changes over time and explore the role of digital technologies as enablers of sustainable innovation in tourism
The development of a comprehensive mine action policy in Ukraine requires the creation of an effective state administration structure. The largescale of the territories of Ukraine contaminated with explosives, the clearance of which must be ensured by the state, requires the search for innovative approaches that will make it possible to solve the problem. To date, the existing mine action structure of Ukraine is unable to respond effectively to the scale and urgency of mine threats. In the field of mine action, there is duplication of functions between state authorities, formulation of inconsistent tasks, parallel functioning of two advisory authorities with overlapping mandates, and lack of proper integration between information systems and state registers. This not only results in additional expenditures for the state budget but also significantly undermines economic security because ofthe inability to promptly and effectively carry out large-scale work to clear agricultural lands. The purpose of this studyis to design an effective state management structure formine action on the basis of a systems approach. By applyingthe methodology for designing organizational structures, which involves grouping system elements according to universal functionsand dividingthe system into a control and managed subsystem with the allocation of the main, auxiliary and service productions in the latter, a state management structure of mine action has been developed. To designthe above structure, differenttypes of mine action,combat, operational and humanitarian, were distinguished, which made it possible to qualitatively determine the place, role and functional purpose of any state authority that is directly involved in the sphere of mine action at present or may be involved in the future. Functional ordering also involves defining "types of work" provided for by the so-called "technological process" in regardto elements of the production level system and "tasks" in regardto elements of the state authority level system. Thus, the developed management structure for combat, operational and humanitarian demining is fully manageable and excludes any duplication of functions or inconsistency of tasks. This is achieved through high-quality regulation of the activities of state authorities and other legal entities of various forms of ownership, whose work is directly or indirectly related to mine action in Ukraine.
