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Social capital as a driver of e-commerce development: Evidence from European panel data analysis
No full textResearch background: E-commerce is a key driver of global economic growth, influenced by technological, economic, and social factors. While prior research has focused on economic and technological determinants, the macroeconomic role of social capital—defined by trust, norms, and networks—remains underexplored. Social capital reduces transaction uncertainty, strengthens consumer trust, and facilitates business expansion, yet its cross-national impact on e-commerce adoption requires further investigation.
Purpose of the article: This study examines how social capital influences e-commerce development in European countries, exploring whether higher levels of social capital contribute to greater e-commerce adoption and transaction intensity while accounting for economic factors such as GDP per capita and disposable income.
Methods: The study analyses panel data from 28 European countries (2015–2023) using the Social Capital Index (SCI) to measure national trust and networks. E-commerce activity is assessed based on online purchase participation and business turnover from digital sales. Panel data models with fixed and random effects are applied to examine the relationship between social capital and e-commerce adoption.
Findings & value added: The results confirm that social capital significantly influences e-commerce adoption. Countries with stronger trust networks and social cohesion see higher participation in digital commerce. Economic conditions, including GDP per capita and disposable income, moderate this effect, with wealthier nations benefiting more from trust-driven e-commerce expansion. The study underscores the importance of fostering social capital to enhance consumer confidence, lower transaction costs, and promote sustainable digital market growth, offering policy recommendations to strengthen trust mechanisms and support e-commerce ecosystems
Digital readiness across the European Union: A multidimensional empirical assessment of structural determinants, intra-regional disparities, and strategic implications in the era of Industry 5.0
No full textResearch background: Digital transformation across the European Union (EU) has become a critical driver of economic competitiveness and societal development. However, there are still notable differences in member states’ infrastructure, governance, and level of digital preparedness. Interest in setting the primary factors that determine digital maturity and understanding the structural disparities in the EU’s digital landscape is developing as a result of Industry 5.0, which places a high priority on human-centricity, sustainability, and resilience.
Purpose of the article: The objective of this article is to evaluate the digital preparedness of EU member states by using the most recent Network Readiness Index (NRI) data for 2024. It aims to determine the factors that have the most significant impact on the overall digital performance, evaluate the disparities between countries in the quartiles of the NRI rankings, and visualize the current status of digital transformation throughout the EU. This research further contextualizes the findings within the shift from Industry 4.0 to Industry 5.0.
Methods: The study applies a combination of multivariate and inferential statistical techniques. A segmentation map is used to visually group countries by digital performance. Stacked bar graphs illustrate the importance of specific NRI components to overall scores. An independent sample t-test is performed to assess significant differences between high-performing and moderately performing countries. A regression study determines the primary predictors of overall digital readiness.
Findings & value added: The study indicates significant variability in the structure and determinants of digital readiness among EU nations. Research and development (R&D) investment, household Internet access, and GDP per capita in purchasing power standards (PPS) are considered the most reliable indicators of digital success. Statistically significant differences were established between the first and second quartiles across various parameters of the NRI. This study improves the literature by delivering a thorough empirical evaluation of the 2024 NRI data and presenting practical recommendations for policymakers aiming to promote fair and sustainable digital transformation in accordance with Industry 5.0 principles
Blockchain-based financial systems: Trust, transparency, and the future of decentralized finance
No full textBusiness environment as a pull factor for migration from highly developed countries: The case of migrants from Sweden, Denmark, and Finland to Germany
No full textResearch background: Migration from high-income countries has gained renewed attention as a significant phenomenon in the context of global labour markets and economic integration. While historical migration from the Nordic countries has been documented, the contemporary focus should shift toward understanding how modern business development factors in Germany act as pull elements. In particular, the quality of human capital and the enhanced probability of attracting highly skilled, entrepreneurial migrants from developed countries are critical aspects. Recognizing and enhancing the potential to gain high-quality migrants can contribute not only to Germany’s economic growth but also to a more balanced and sustainable migration process across high-income regions.
Purpose of the article: This paper aims to explain the migration flow from three high-income Nordic countries — Sweden, Denmark, and Finland — to Germany. The analysis specifically focuses on the influence of Germany’s business environment on attracting migrants with high-quality human capital and entrepreneurial potential.
Methods: We employ the gravity emigration model along with Fixed Effects (FE) and pooled Ordinary Least Square (OLS) estimation techniques, using data covering the period 1999–2018. This methodological framework enables us to quantify the impact of business development indicators on migration flows and to account for both economic and socio-demographic factors.
Findings & value added: Our results indicate that, despite Germany’s comparatively lower rankings in certain business indicators relative to its Nordic neighbors, its robust business development policies serve as a substantial pull factor for migrants from high-income EU member states. This finding underlines the importance of a well-developed business environment in attracting high-quality migrants. The paper contributes to the literature by highlighting that enhancing Germany’s business infrastructure and support mechanisms can further improve its capacity to attract talented and entrepreneurial individuals from highly developed countries — a priority for long-term sustainable economic growth and integration within the EU
Social commerce use intention: The mediating effect of trust and the moderating effect of generational cohorts
No full textResearch background: Social commerce represents a quickly growing field with significant implications for e-commerce and marketing. It has been gaining importance in recent years due to the popularity of social media and online shopping. The evolving nature of social commerce impacts consumer behavior. Despite evidence that age cohorts exhibit distinct digital behaviors, generational differences in s-commerce adoption remain underexplored.
Purpose of the article: This study examines the role of communication via social media, prior online experience, and trust in shaping s-commerce use intention. It specifically investigates generational differences between Generation X (Gen X) and Generation Y (Gen Y), addressing gaps in existing research on trust and digital engagement.
Methods: A quasi-representative sample of Gen X and Gen Y respondents was analyzed using multi-group structural equation modeling. The study tested a mediation model, evaluating whether trust mediates the relationship between communication, experience, and s-commerce use intention across generations.
Findings & value added: The results highlight significant differences and similarities in consumers\u27 intention to use social commerce between generations. Trust mediates the adoption process only for Gen X, while Gen Y relies more on direct social media interactions. This suggests that older consumers prioritize trust-building mechanisms, whereas younger consumers are more influenced by social engagement and peer interactions. This study extends s-commerce adoption models by incorporating generational cohort theory, offering practical insights for businesses to optimize trust-building and communication strategies based on generational preferences. The findings support targeted strategies to enhance customer engagement and increase s-commerce adoption in emerging markets
The impact of AI pilot zones on market competition: Causal insights from policy implementation
No full textResearch background: Artificial intelligence (AI) is a major catalyst for innovation and economic transformation, reshaping competition across industries. In 2019, China launched AI pilot zones to stimulate AI innovation while managing its economic and societal impacts. These zones aim to foster AI ecosystems through targeted investments, infrastructure, and favorable policies. However, concentration of resources and market power within these zones raises concerns about their effects on competition. This study examines how AI pilot zones influence competitive dynamics, focusing on the mechanisms driving market concentration and firm behavior.
Purpose of the article: The causal impact of AI pilot zones on market competition in China is examined. A difference-in-differences (DID) methodology is used to explore the impact of these zones on firm entry, market concentration, and innovation. This study seeks to understand if pilot zones enhance competition by lowering entry barriers and supporting innovation or they exacerbate market concentration by favoring large firms.
Methods: The DID methodology uses firm-level data from AI pilot and non-pilot regions (2010–2023). Market competition is assessed through concentration ratios based on revenue and income from top firms. Robustness tests, including placebo tests, and instrumental variable approaches are applied to ensure the reliability of results. Agency theory and resource dependence theory are used to explain the mechanisms through which AI policies impact competition, specifically access to resources, information asymmetry, technological diffusion and government support.
Findings & value added: The study findings show that AI pilot zones positively influence competition by reducing market concentration and encouraging new firm entry. While larger firms benefit initially from policy support, the long-term effects include a more competitive market with greater firm diversification and innovation. Mechanistically, this study identifies that policy-induced enhanced information transparency, mitigated financial constraints, targeted government subsidies, and accelerated technological diffusion enable smaller firms to compete more effectively. This support helps to overcome entry barriers by providing critical resources, thereby encouraging the entry of new players and facilitating innovation. Furthermore, the policy creates a competitive environment where even incumbents must innovate to maintain their market position. The study highlights that AI pilot zones, while initially benefiting larger firms, contribute to a more competitive market by diffusing technological advancements and resources equitably across firms. This research contributes empirical evidence on the role of AI policies in shaping competition and offers valuable insights for policymakers aiming to balance innovation and competition in AI-driven economies
The algorithmic management of job loss and creation in the enterprise generative, multimodal, and agentic artificial intelligence economy
No full textResearch background: Enterprise generative, multimodal, and agentic artificial intelligence (AI) technologies facilitate transformative productivity and workforce adaptation gains in innovative organizations, redesigns autonomous team and talent management for workforce and job rotation planning, skill development, and career paths, handle context-specific collaborative business processes, workflows, and decision-making, and augment multi-agent system scaling for labor productivity and operational efficiency, redefining agile and adaptive organizational performance in dynamic business environments, driving interoperable big employee data and strategic decision management, and creating strategic fluidity and synchronized digital labor for sustainable business value. Connected and interoperable agentic AI systems can carry out multistep tasks autonomously, reduce operational costs and unemployment rates, and manage big data-based organizational workflows and management pipelines, driving business value creation and productivity gains, reallocating digital labor, and redefining employee experiences and labor markets in terms of job loss and creation by upskilling and retraining. AI labor impacts predictions are based on multimodal data and labor force productivity modeling in relation to how job and skill creation can affect economic conditions and workforce development, while driving business model transformation.
Purpose of the article: We aim to clarify whether enterprise generative, multimodal, and agentic AI-based task automation and machine performance complements technology-driven employment changes and algorithmic efficiency, resulting in workforce reduction and competitive pressures due to economic incentives in terms of how i) deep reinforcement learning algorithms can build digital agentic workflows for autonomous Internet of Things (IoT) sensor-based industrial robotic machines, leading to employment relation, personnel retention and recruitment, work reorganization, and labor productivity optimization, engaged productive staff flexibility and autonomy, and job performance and satisfaction, ii) how task automation and augmentation disrupt labor markets and reshape workforce for either more layoffs or more new hires, predicting both increased or lower wages, high or decreased unemployment, and job creation or elimination, and iii) how computer vision-based task automation and augmentation technologies redesign business-critical workflows and workforce upskilling processes across collaborative enterprise IoT and sluggish hiring environments for task automation and augmentation, streamlining personalized human resource support, resource efficiency, and enterprise productivity, driving economic growth.
Methods: A quantitative literature review of ProQuest, Scopus, and the Web of Science databases was carried out and the most relevant research published between 2024 and 2025 was identified and analyzed. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) and the web-based Shiny app were harnessed for search results and screening. Dimensions (for bibliometric mapping) and VOSviewer (for layout algorithms) were the deployed data visualization tools. Evidence synthesis screening software and reference and review management tools leveraged included AMSTAR, CADIMA, DistillerSR, JBI SUMARI, MMAT, Nested Knowledge, PICO Portal, and SRDR+.
Findings & value added: The main value added derived from the systematic literature review is that enterprise generative, multimodal, and agentic AI system applicability correlates with occupational task operation completion, wage, employment prospects, and education, driving business choices and transformation, labor markets, and economic growth. The benefits for theory and current state of the art are that enterprise generative, multimodal, and agentic AI-based flexible work arrangements and increased employee tracking for organizational and workforce performance can improve job quality while reducing pay inequity, staff absenteeism, job turnover, and widespread unemployment, affecting labor markets and resulting in long-term business values and outcomes. Occupational AI and computer vision technologies impact predictions with regard to work activity automation and augmentation in terms of job loss, labor productivity, and wage raising or lowering. Policy implications reveal that employee productivity and performance tools entail job displacement and creation, requiring emerging workforce reskilling or upskilling for talent attraction, retention, progression, and promotion across structural labor market transformation
Catallaxy: Between knowledge and the pretence of knowledge; between freedom and justice
No full textMotivation: The apparent consistency or complementarity between freedom and justice as norms defining the space of economic performance is marked by the nature of information networks uniquely obtained under conditions of complete openness, allowing people to pursue their interests through ingenuity and the possibility of unlimited cooperation.Aim: Starting from the central area of Hayek’s discernment, which was knowledge produced and acquired in the market as a result of free decisions made by economic agents focused on the pursuit of individual goals, an attempt will be made to present this perspective on the tension between the values of freedom and justice so fundamentally encroaching on contemporary political and economic discussions.Materials and methods: Critical analysis of text.Results: Hayek did not remain blind to the negative consequences of the liberalism he promoted, although he did not develop an elaborate version of measures to neutralise them. His hints are found in this regard, but remain only general. Perhaps the only ones possible for such trans-social issues. And even in the face of revealed data regarding income inequality, we will not deny that entrepreneurs are the most productive and creative economic units — individuals who, by natural motives, will fight and utilise their abilities under the freedom of competition to establish and maintain their market positions. In a society, the state is the only force opposing these naturally grown potentials — it is only effective with the proper institutional setting for each country
Implementation of ESG principles in the water supply sector in Poland: An empirical data analysis
No full textPurpose: This study investigates the integration of ESG (Environmental, Social, Governance) principles into the water supply sector in Poland from 2021 to 2023. It explores the degree to which selected environmental and social indicators reflect strategic improvements aligned with sustainable infrastructure development and examines whether proxy indicators can be effectively used to infer governance quality in the absence of formal institutional data.
Design/methodology/approach: The research employs a quantitative, analytical-comparative methodology, utilizing statistical data sourced from the Local Data Bank of Statistics Poland (BDL GUS). ESG-related indicators were categorized, standardized, and analyzed using descriptive statistics and time series analysis. The governance dimension was evaluated through proxy indicators derived from infrastructural and operational metrics.
Findings: The study reveals consistent improvements in environmental efficiency and social accessibility within the sector, including reductions in water losses and network failures, alongside an expansion of infrastructure. Furthermore, the findings support the hypothesis that these developments are indicative of effective public governance, operational planning, and alignment with the objectives of SDG 6.
Practical implication: The results offer policymakers and public utility managers a structured method to assess ESG compliance in data-constrained contexts. By demonstrating the feasibility of proxy-based governance evaluation, the study provides a model for extending ESG reporting frameworks to other sectors lacking institutional transparency.
Originality/value: This work presents one of the first systematic ESG analyses of the Polish water supply sector using national public data. Its methodological innovation lies in applying governance proxies and interpreting ESG coherence across multiple dimensions. The study holds relevance for scholars, regulators, and infrastructure operators seeking to advance sustainable and accountable public service models
Correspondence analysis in marketing research
No full textThe paper presents the theory and an example of the use of correspondence analysis in marketing research. Calculations were performed using the STATISTICA PL software package