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Digital twin-based virtual factory and cyber-physical production systems, collaborative autonomous robotic and networked manufacturing technologies, and enterprise and business intelligence algorithms for industrial metaverse
No full textResearch background: Cognitive computing and robotic technologies, enterprise digital twin system modeling, and sensory perception algorithms optimize industrial big data exchange and production collaboration, production floor management, and smart device 3D simulation and visualization in the Industry 5.0 metaverse and virtual shop floor environments. Enterprise metaverse business operations, multi-granularity cognitive computing, and industrial big data fusion simulation integrate virtual and augmented reality technologies, collaborative robotic and industrial cyber-physical production systems, and artificial intelligence-enabled edge computing and Internet of Everything devices in mobile edge computing environments. Cloud-based production and digital twin Internet of Things networks, 3D immersive virtual reality and realistic 3D scene construction technologies, and cyber-physical production and business process management systems articulate smart production engineering and management, artificial intelligence-driven physics simulation, and Internet of Things-based robotic manufacturing in highly realistic industrial product representations and 3D virtual spaces with regard to big data-driven business decisions.
Purpose of the article: We show that 3D immersive virtual reality and digital twin metaverse technologies, spatial scanning modeling, and autonomous robotic and virtual factory simulation systems are pivotal in immersive 3D process management, industrial manufacturing production value, and knowledge accumulation in synthetic simulated environments. 3D simulation-based industrial processes and immersive experiences can be attained through cognitive computing and robotic technologies, multi-modal information fusion, autonomous intelligence generation, and multiple production process management in immersive 3D metaverse environments. Immersive, multisensory, and augmented digital experiences can be attained through 3D factory simulation and immersive extended reality technologies, cognitive robotic process automation, autonomous robotic and industrial machine learning systems, and task allocation optimization in computer-generated 3D virtual environments.
Methods: We analyzed and synthesized common operations for the first 60 companies in industrial metaverse on ensun (AI-based supplier sourcing tool’s) website in terms of key takeaway, working industry, type of company, and specialized areas, and identified three main topics.
Findings & value added: The main value added derived from our research is that industrial metaverse 3D simulation and modeling, digital twin and remote fault diagnosis technologies, multiphysics simulation and predictive maintenance tools assist industrial big data monitoring and management, Internet of Things-based robotic manufacturing, and multiple processing tasks in 3D digital twin factories. Collaborative autonomous manufacturing operations, artificial intelligence-driven physics simulation, and smart industrial devices and processes necessitate industrial metaverse decentralized federated learning, cognitive computing and robotic technologies, and cognitive digital twins in virtual shop floor environments, generating economic value. 3D simulation and visualization technologies, business intelligence and digital twin-based cyber-physical production systems, and big data-driven forecasting and real-time collision detection algorithms can be harnessed in robotic automation processes, intelligent manufacturing upgrading, and sustainable industrial value creation across 3D digital twin factories and distributed computing environments
The triple threat: Understanding the effects of cyber threats, corruption, and money laundering on the business environment
No full textResearch background: Cyber threats, corruption and money laundering are interconnected factors that pose significant challenges to the business environment. Their impact varies based on a country\u27s economic development and the effectiveness of countermeasures. Despite global and national efforts to combat these threats, their combined influence on business conditions requires further examination.
Purpose of the article: This study aims to analyse the impact of cyber threats, corruption, and money laundering on the business environment across different country groups. It identifies the most vulnerable aspects of business to triple threats and highlights countries exhibiting anomalies in their ability to counter these illegal practices.
Methods: The research utilises data from 125 countries, incorporating the Basel Anti-Money Laundering Index, the National Cyber Security Index, and the Corruption Perceptions Index. Correlation analysis established statistically significant relationships between these threats and business conditions. Cluster analysis identified three country groups based on GDP, ease of doing business, and countermeasure effectiveness. Canonical analysis determined the most affected business sectors, while neural network modelling revealed countries with exceptionally high or low effectiveness in combating these threats.
Findings & value added: The most affected areas include tax payments, international trade, contract enforcement, electricity access, and insolvency procedures. Among developed countries, Denmark, Finland, and Norway demonstrate high effectiveness in countering these threats, whereas Bulgaria, Cyprus, and Greece show lower efficiency. In developing nations, China, Thailand, and Kazakhstan exhibit strong countermeasures, while Egypt, Ghana, and Grenada lag behind. Among the least developed countries, Mozambique and Nicaragua show high effectiveness, while Venezuela and Yemen fall into the low-performance category. These findings provide a foundation for enhancing national policies and strategies to strengthen economic security and resilience against financial crimes and cyber threats
Top innovation EU member states based on European patenting: Politicians, academia and AI got it wrong?
No full textResearch background: The global highly competitive society depends upon innovations. The investment in research and development (R&D) should support endeavors leading to inventions to be protected by patents and to become innovations. The EU is aware about it and wants 3% of GDP to go into R&D (GERD Index) in order to reach competitiveness based on innovations protected by European patents (EPs).
Purpose of the article: Politicians, academia and Artificial Intelligence (AI) believe that the GERD Index and the absolute and relative number of EPs are the key criteria to select top innovation EU member states. The purpose of this article is to verify it.
Methods: The Eurostat, World Bank, and WIPO data for 2014–2023 is double checked. Conventional criteria to select top innovation EU member states based on EPs and endorsed by AI (GERD Index, EPs) are employed along with newly proposed criteria reflecting longitudinal trends in applications and granted EPs within the three-year cycle, their cost and success rate.
Findings & value added: A deeper understanding of inventing-patenting dynamics reveals that the innovation leadership based on EPs should consider conventional investing and patenting factors as well as other factors and their trends. Conventional criteria lead AI to point to Germany and Sweden, but the newly proposed factors and their criteria show that the innovation leadership based on EP is much more complex and that the GERD Index and EPs trends, the distinction of EP applications v granted EPs, their concentration factor and their cost factor points to Sweden and Denmark, Finland, and the Netherlands and their success rate to Italy, while Austria, Belgium, and Germany are some ways back. The qualitative consistency, focus, determination and lean efficiency might be even more important than the quantitative country size, GDP and GERD
Barriers to digital services trade: Evaluation of their restrictiveness with application of Brier Score
No full textResearch background: The improvement in digital transmission has driven the rapid development of digital services trade; however, it comes with more barriers to it. International comparisons of the restriction levels of digital services trade are crucial for promoting its liberalization, thereby obtaining economic gains. To provide a reliable measurement of barriers to digital services trade, it is necessary to construct a reasonable evaluation index system and ensure the objectivity of the determination of measurement indicator weights.
Purpose of the article: This study aims to develop a novel method for measuring the restrictiveness of barriers to digital services trade, revealing the current barriers to digital services trade in various economies.
Methods: This study establishes an evaluation index system for barriers to digital services trade by using the Brier Score to screen original evaluation indicators. Then, based on the established evaluation index system, the Brier Score is used to hierarchically determine the weights of evaluation indicators. Additionally, it compares the barriers to digital services trade from the total evaluation index and the evaluation index in five policy areas.
Findings & value added: Comparative analyses show that most economies have restrictive policies in digital services trade, and they are mainly implemented through two policy areas: infrastructure and payment system. In these two areas, economies face barriers related to digital infrastructure, discriminatory access to payment settlement methods. We also make methodological contributions by using a novel method to measure the restrictiveness of barriers to digital services trade. To the best of our knowledge, no study has integrated the Brier Score with trade policy analysis. Our method improves the overall trade restriction evaluation capability of the evaluation index system and ensures that the weights reflect the level of trade restrictions on the measurement indicators
Public acceptance of autonomous and remotely piloted drones in civil and military domains: Socioeconomic, political, and safety correlates
No full textResearch background: Although drone acceptance research is extensive, key gaps remain in understanding how socio-demographic, educational, and attitudinal variables intersect to shape public perceptions. Existing studies frequently address drones’ ethical and legal dimensions but seldom explore how gender, education, and political trust collectively inform acceptance, particularly in comparing civil versus military, remotely piloted versus autonomous technologies.
Purpose of the article: This study investigates public perceptions of drone technology, societal safety, and trust in governance, with a special focus on how demographic and educational factors influence attitudes and behaviors. It aims to illuminate determinants of drone acceptance—including safety perceptions and endorsement of police-drone initiatives—and offer policy-relevant insights into integrating novel technologies responsibly.
Methods: A quantitative survey of 1,250 Czech respondents (15+) employed quota sampling to capture diverse demographic and educational backgrounds. Participants completed a 50-item electronic questionnaire, yielding data on drone acceptance across varied contexts (e.g., infrastructure surveillance, military operations). Binary logistic regression and descriptive statistics were used to analyze associations between socio-demographic variables, perceived safety, and support for drone use in public order and traffic enforcement.
Findings & value added: Results indicate that males more readily accept autonomous drone operations in critical infrastructure, while females exhibit higher uncertainty overall. Education strongly conditions opposition, with university-educated respondents more critical of lethal drone use but more trusting of democratic and expert-led governance frameworks. The regression analysis reveals that neither age nor active opposition to drones significantly reduces perceived safety. Rather, awareness of existing police-drone programs and agreement with their use significantly enhance individuals’ sense of safety. These findings highlight the importance of transparent communication, targeted outreach for women and less educated groups, and dedicated support for mid-sized communities. In addition, by synthesizing recent business and logistics scholarship, the study demonstrates that drones’ broader economic value is highly conditional: viable in hybrid delivery systems, infrastructure-sensitive in urban logistics, and legitimacy-dependent in marketing and construction sectors. This combined perspective advances both theoretical and policy debates, linking public acceptance with economic feasibility
Prediction models reloaded: Advanced insights for SMEs in the Bucharest Nine countries
No full textResearch background: Financial health is an essential factor in the success of an enterprise, its survival, competitiveness in the market and sustainable development. Therefore, predicting constraints, weak points and possible risks that could cause financial distress is crucial. Small and medium-sized enterprises (SMEs) remain a key pillar of any prosperous economy during every phase of the economic cycle, particularly in emerging countries, such as the Bucharest Nine.
Purpose of this article: The objective is to specify indicators of the financial health of SMEs depending on the economic cycle through unconventional incentives under the conditions of the Bucharest Nine. It entails a longitudinal mapping of more than 30,000 enterprises during the pre-crisis, crisis and post-crisis periods, as along with data on economic growth.
Methods: Financial statements from the Orbis database, covering the period 2018–2023, were used to create a robust final sample of SMEs. Logit least absolute shrinkage and selection operator with 10-fold cross-validation was employed to identify bankruptcy predictors from 75 origin predictors, including liquidity, activity, profitability, indebtedness, earnings management and business development. The resulting models for each period were validated on a test sample of prosperous and non-prosperous enterprises. Furthermore, the classification ability of all models was evaluated using the area under the receiver operating characteristic curve.
Findings & value added: This research adds value by demonstrating important factors that influence the bankruptcy of SMEs and guiding financial managers to focus on these factors based on the expected economic cycle. Thus, developed prediction models are particularly beneficial for businesses themselves, enabling them to predict financial health depending on the expected state of the economy, which helps overcome the existing animosities of businesses towards predictions. The results of the present study may also prove valuable to agencies dealing with SMEs, financial database providers or auditing companies. The present study enhances the idea of financial distress prediction by including unconventional financial indicators, including earnings management and value-added variables, in traditional bankruptcy modelling frameworks. This innovative combination enhances the theoretical framework of financial economics by providing a more dynamic and context-aware method for assessing SME sustainability over the economic cycle
Turning green into gold: How does green total factor productivity boost economic growth?
No full textResearch background: Balancing economic expansion with environmental sustainability has become a central policy challenge. Green total factor productivity (GTFP) integrates environmental constraints into productivity analysis and is increasingly used as a measure of green growth. However, evidence on how GTFP shapes macro‑level economic performance remains limited, with existing research largely confined to a small number of single‑country studies.
Purpose of the article: This study aims to investigate the causal effect of GTFP on economic growth using a global sample of 150 countries. It further seeks to identify the key transmission mechanisms through which GTFP influences macroeconomic outcomes and examines the moderating role of household savings rates in this relationship.
Methods: Using a macro panel dataset for 150 countries from 2014 to 2023, this study first measures GTFP with a machine learning-enhanced three-stage slack-based measure-data envelopment analysis model combined with the global Malmquist productivity index. Subsequently, a double/debiased machine learning (DDML) model is employed to estimate the causal impact of GTFP on economic growth, effectively addressing the challenges of high-dimensional confounders and nonlinearities present in the data.
Findings & value added: The results demonstrate a significant and robust positive relationship between GTFP and economic growth. This effect is primarily transmitted through two channels, which are enhancing exports and increasing household consumption. Furthermore, a high household savings rate is found to amplify the positive impact of GTFP on economic growth, validating the ‘Golden Rule’ savings rate proposition. This study contributes to the literature by providing the first large-scale, global evidence on the macroeconomic benefits of improving GTFP. By identifying specific transmission pathways and moderating effects employing DDML techniques for causal inference, this study offers empirical insights for policymakers to design effective green growth policies
Does AI application make enterprises productivity higher? From the perspective of employee human capital upgrading
No full textResearch background: With the rapid development of new‑generation digital technologies, including big data, blockchain, and artificial intelligence (AI), the deep integration of AI into traditional industries has induced unprecedented economic changes. The development of AI technologies requires complementarity between capital and highly skilled employees, and the optimization of enterprise human capital structures warrants investigation; therefore, AI has significant potential to improve employees’ human capital and enhance enterprise productivity. This study reveals the economic consequences of AI, examines the internal logic of optimizing enterprises’ labour structures amid AI’s rise, and explores how the application of AI affects enterprise productivity at the enterprise level, as well as the role of upgrading human capital in this process.
Purpose of the article: This study aims to explore how AI application impacts enterprise productivity, as well as the mechanism by which such an impact is achieved through the enhancement of human capital. To begin with, we integrate advanced academic research to explore the theoretical relationships among AI applications, human capital upgrading, and enterprise productivity, thereby clarifying the inherent drivers and practical methods for boosting enterprise productivity. Subsequently, the study employs a dataset of 5,167 listed companies from the Shanghai and Shenzhen A‑share markets to investigate how the adoption of AI affects corporate productivity through the lens of human capital enhancement. Furthermore, we offer strategic recommendations to adjust the workforce configuration and boost corporate efficiency by improving human capital quality and promoting AI applications.
Methods: Using panel data from 3,646 Chinese A‑share listed companies for the period 2011–2024, the study applies machine learning methods to generate an AI dictionary and investigates the relationships among enterprise AI application, human capital upgrading, and productivity.
Findings & value added: Theoretical exploration indicates that AI applications will increase demand for high‑skilled labour and crowd out some low‑skilled labour to optimize the human capital structure, thereby improving enterprise productivity. A mechanism test reveals that AI applications enhance enterprise productivity by upgrading human capital. Heterogeneity analysis suggests that the impact of AI application on productivity is more significant for non‑state‑owned, small‑ and medium‑sized, and non‑technology‑intensive enterprises. This research applies machine learning methods to generate an AI dictionary from a text‑analysis perspective and constructs AI application indicators at the micro‑enterprise level. At the same time, from the perspective of human capital upgrading, the study analyzes the impact of AI applications on enterprise productivity and provides a more reasonable explanation for the improvement of enterprise production efficiency in the AI era
Methodological assumptions of the New Institutional Economics: A reconstruction of the approach
No full textMotivation: The emergence of the New Institutional Economics (NIE) was a response to the methodological limitations of neoclassical economic models, which lacked explanations concerning the costs of using the market mechanism, the structure of property rights, or institutional evolution. NIE enriches the neoclassical approach by embedding its research models in social realities and focusing on institutions as fundamental variables of economic processes.Aim: This article aims to reconstruct and discuss the methodological foundations of NIE, with particular emphasis on assumptions regarding individual rationality, methodological individualism, and opportunism among transaction partners.Materials and methods: This paper adopts a theoretical-analytical approach based on a literature review. A comparative analysis and an attempt to identify common elements of the institutional research approach were applied.Results: Using the research method, the fundamental methodological assumptions of NIE were reconstructed. These assumptions bring neoclassical economics closer to the realities of socio-economic processes