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As a new engine to promote global economic and social development, information and communication technology (ICT) plays a key role in the field of modern economy. The aim of this paper is to investigate the nexus between ICT and green total factor productivity (GTFP) on a global scale. An extended data envelopment analysis model (DEA), named WINDOWS-US-SBM, was constructed for calculating the GTFP of 65 countries from 2007 to 2019. This paper empirically analyzes the spatial effect and the transmission mechanism of ICT development on GTFP in countries from different income groups. The results show a prominent imbalance between ICT development and GTFP in various countries. ICT can effectively improve GTFP and play a crucial role in lower middle-income countries. The development of ICT can improve GTFP through technological progress, energy intensity, and trade openness. This paper is helpful to provide policy guidance for the development of ICT and give a new perspective of global green development. First published online 20 May 2024

The Circular Economy (CE) is receiving more attention, especially in Industry 4.0 (I4.0). In the face of several ambiguous and uncertain information, fuzzy techniques based on Fuzzy Set Theory (FST) are essential for developing CE strategies. This paper uses bibliometric methods to analyze the characteristics of the authors, nations/regions, institutions of the literature of FST and CE, and the collaborations relations between them, and then summarize the literature on fuzzy techniques in the CE and identify the specific role that FST can play in each stage of CE, its primary effects on the CE’s pre-preparation stage, design and production stage, and recycling and reuse stage. Meanwhile, the paper explores the advantages of I4.0 technologies for CE and analyzes the research on the role of fuzzy techniques based on FST for CE and I4.0 technologies. Last but not least, this paper is concluded by summarizing the knowledge gained from the bibliometric and content analyses of the literature and suggesting further research directions of investigation. This research will draw attention to FST’s contribution and encourage its advancement in CE and I4.0 technologies.

The issue of pensions has become increasingly topical. This paper presents the ordered weighted averaging real average pension (OWARAP) operator. The OWARAP operator is based on the ordered weighted averaging (OWA) operator and calculates the future average retirement benefit taking into account price changes. Moreover, this work extends the OWARAP operator by using order-inducing variables, generalized means, and probabilities. This paper ends by analyzing the applicability of the OWARAP operator and its extensions in forecasting the real average Social Security benefits for retired workers in each state of the United States (U.S.). The results demonstrate the usefulness of the proposed approach in retirement decision making. First published online 30 April 2024

The paper explores the impact of digital transformation (DT) on new business creation in the case of European Union member states over the period 2015–2020 by employing several econometric techniques such as Ordinary Least Squares (OLS) and quantile regression (QR). The results of OLS regression indicate a positive and significant link between DT and entrepreneurial activity. However, the quantile regression results highlight a parameter heterogeneity in the effect of DT on entrepreneurial activity. Furthermore, the magnitude of the impact is greater at the higher size quantiles of the new business density distribution. Overall, the empirical findings highlight the key role of DT in developing public policies aimed to spur entrepreneurial activity. Thus, this paper brings significant contributions to the extant literature on the macro determinants of firm creation.

This paper sets out a methodological framework for identifying and mitigating unemployment problems in different regions. The study is based on the 2020 and 2023 Latvian Unemployment Surveys. The study includes a comprehensive review of the relevant scientific literature, policy and regulatory frameworks, the development of a multilevel research methodology and tools, an analysis of the research results, in-depth discussions of the identified problems in the context of existing scientific knowledge, and a set of key conclusions and proposed measures to address these problems. By structuring the paper in this way, it offers a holistic and systematic approach to understanding and addressing regional unemployment problems. The study contributes to the ongoing dialogue on regional economic disparities and highlights opportunities for more inclusive, sustainable and equitable labour markets.

Recently, sustainable economic performance (SEP) has been the foremost requirement due to uncertainty of the economy and environment. This issue needs best practices and governance and also needs researchers’ focus. Thereby, the paper aims to assess green supply chain (SC) practices, green SC governance and their role in the achievement of SEP in the manufacturing sector of China. The paper also attempts to explore the moderating effect of organizational support in proposed framework. PLS-SEM model was employed to test model’s reliability and validity. The very same method has applied to test the linkage among outlined constructs. Obtained results confirms the moderating impact of organizational support among traditional governance, relational governance and the achievement of SEP in the manufacturing industry in China. This article provides guidelines to the regulators in developing policies related to the achievement of SEP using green SC practices and governance.

Social sustainability is a global necessity because of environmental and economic uncertainty. This issue needs the foremost solution, and for this purpose, researchers’ and policymakers’ emphasis is required. Thereby, the present paper investigates sustainable energy technologies such as solar and hydroelectric, eco-innovation and EG and their impact on social sustainability in China. The study also used industrialization and inflation as the control variables in the time span of 1981 to 2020. The present study also applied the Dynamic Auto-regressive Distributed Lags (DARDL) model to evaluate the association between the outlined variables. The results indicated that sustainable energy technologies such as solar and hydroelectric, eco-innovation, economic growth, industrialization and inflation are significantly associated with social sustainability in China. The present paper offers standard policies to regulators in making regulations related to maintaining social sustainability by using effective sustainable energy technologies and eco-innovation.

The paper aims to examine individuals’ behavior at work and/or in college, by placing the focus on employability, proactiveness and socioeconomic status as predictors. The paper’s objectives are threefold: (1) to examine if there is a causal relationship between employability and proactive personality of individuals and their citizenship or deviant behavior at work and/or in college; (2) to understand the extent to which socioeconomic status influences individuals’ citizenship or deviant behavior at work and/or in college; and (3) to determine the mediation role of socioeconomic status in the causal relationship between employability and proactiveness and individuals’ behaviors. Linear regression models were employed to test the predictive power of the variables once a causal relationship had been confirmed. Mediation tests were used to quantify the extent to which a variable participated in known causal relationships. The research reports results based on data collected from 534 university business students in Romania. The results confirm that employability and proactiveness predict workplace behavior, and that socioeconomic status mediates this relationship.

Extensive analysis of intertwinement with other industries caused the energy sector to gain momentum in the recent economic literature. This paper aims to create an indicator that captures the impact of financial stability for energy companies on all other industrial groups. To this end, we use daily data from 2007 until the end of 2021 to compute financial stability metrics for all European companies from the STOXX 600 index. The main contribution of our study is to harness the neural network forecasting power to predict extreme levels of this impact. We motivate this choice with evidence from the literature that documents the improved performance of these methods in predicting crises. Our methodological approach also employs an outlier detection algorithm based on copula (COPOD) to identify situations when the energy sector substantially impacts other industries and develop a framework to predict out-of-sample situations. We found evidence that the Deep Renewal model has superior forecasting accuracy to the standard Croston model. The main conclusion is that the design of this methodological framework allows authorities to monitor the impact of shocks produced by the energy sector on financial stability at the European level and undertake strategic management actions.

Purpose – This article focusses on the impact generated on GDP growth by the number of graduates in the ICT domain of Romanian counties in the period 2014–2020. Research methodology – We have collected the number of ICT graduates, the GDP per inhabitant in EUR per Romanian county, the number of ICT employees, the number of information service activities employees from the National Institute of Statistics (Romania) and Eurostat. Additionally, we consolidate the correlation matrix and regression analysis for previous statistics indicators. Findings – We have revealed a strong correlation (85,77%) between the number of ICT graduates and the GDP per inhabitant in EUR per Romanian county, and very strong correlations between the number of ICT graduates and the number of ICT employees (94,80%) and the number of employees of information service activities (97,38%). Research limitations – The data set from the National Institute of Statistics (Romania) does not contain entries for every county, due to its statistical methodology, and other socioeconomic phenomena which contribute to the development of the ICT field were just enumerated, such as natality, immigration, and emigration. Practical implications – The study revealed that investment in ICT education (number of graduates) contributes to the local prosperity of communities, counties, and, in general, at the national level as GDP growth. Romanian government can utilize the outcomes of this paper to engage additional resources, funds from Ministry of Education, to develop a long-lived learning culture of ICT skills for the entire population. Originality/Value – The article methodologically highlights the most important ICT hubs in Romania, which are correlated with the existence of an ICT educational infrastructure (high schools and universities) and the measure of impact on GDP growth for the number of highly skilled graduates in the ICT domain. The selected national and European indicators contributed to the novelty of this study.

Purpose – This paper investigates the mediating role of green brand loyalty between green brand image and word-of-mouth (WoM) and the moderating role of green promotion and brand social responsibility among Vietnamese youth. Research methodology – Using an online questionnaire from 1st October 2022 to 31st December 2022, 740 valid responses were collected. Findings – The study reveals that green brand loyalty mediates the relationship between brand trust and WoM. Additionally, green promotion significantly moderates the relationship between brand trust and green brand loyalty. The study concludes that green promotion is vital in influencing consumers’ trust in the brand, subsequently increasing their loyalty. Research limitations – This study has not categorized the groups of environmentally friendly products used by young Vietnamese to see if there is a difference. Practical implications – These results provide significant implications for brand managers to enhance the effectiveness of WoM. Originality/Value – Green promotion is an effective strategy for brands to regain consumers’ loyalty and maintain a close relationship between consumers and green brands. Additionally, green brand loyalty emphasizes the significance of green promotion for developing green companies and can serve as a conduit between customers and brands.

This article primally explores the short-term fluctuation and long-term implications of the international Bitcoin price (BP) on the Chinese green bond (GB) market, within the sample period of 2014:M10–2023:M07. Bitcoin is the most important cryptocurrency and has a carbon-intensive feature, and its price suffers from great volatility and is closely related to the green finance market. Meanwhile, although China is the largest bitcoin mining state, it is pursuing a dual carbon target, which promotes its green bond market’s development. Thus, it is valuable to investigate the influence of BP on GBs in China. Based on the quantile autoregressive distributed lag approach, this paper indicates that the positive and negative impacts of BP on the GB market are significant in the long-term but not apparent in the short-term. These results emphasize the importance for market participants to obtain a better understanding of how BP affects GB under various market circumstances. Implementing specific policies, such as regulatory mechanisms for Bitcoin trade, market-oriented reform for the bond market, and information disclosure, can alleviate shocks from BP and accelerate the development of the GB market. First published online 20 May 2024

Brexit has undeniably had effects on power relations in the world economy and beyond. This article aims to measure the dimensions of power for the European Union, the United States and the BRICS countries and to provide a case study about the prospective impact of Brexit on the EU’s and the UK’s positions within the global architecture of power. In this regard, the paper proposes a new method of measurement of the world power by using six dimensions of power, using data before the Brexit procedures started. The highlights of the empirical findings are that the exclusion of the UK has strengthened EU’s position only for the economic dimension of power, while for the UK, the most dominant repositioning within the power architecture due to the Brexit is recorded for the political & governance. The findings can contribute to raising awareness among policy–makers, companies, institutions and the population, both at European and at national level, about populist tendencies and attitudes towards potential separations of some member countries from the European community and their consequences for the European development from multiple perspectives.

Digital economy is a great route to promote the efficient utilization of natural resources and promote sustainability due to its high-tech, rapid growth, extensive penetration, deep integration and other characteristics. Existing study on the influencing factors of the digital economy is not deep enough and lacks the analysis on the relationship structure of factors influencing the digital economy, which is not conducive for an overall grasp of the digital economy. To correctly understand how to better develop the digital economy, this paper studies its influencing factors and the relationships between them. Based on the time-series data of China from 2002 to 2018, grey correlation analysis was applied to calculate the correlation between these influencing factors and the digital economy, and determine the major influencing factors of digital economy development in China. The Granger causality test and a review of existing research were used to judge the interrelationship of various factors. The interpretative structure model was utilized to determine the relationship structure of the main factors affecting the development of China’s digital economy. The results show that the number of digital talents, state of the technology market, and degree of digitalization are direct influencing factors of the digital economy. The results help to better understand the development of the digital economy and will enable the implementation of policies to improve towards more sustainable cities.

The increasing intensity and frequency of extreme climate events have made improving the adaptability to extreme climate events a strategic imperative for manufacturing companies. This paper investigates whether manufacturing enterprises increase green technology innovation affected by different extreme climate events. Based on panel data of Chinese listed manufacturing enterprises, we show that extreme precipitation events can positively promote green technology innovation, yet extreme temperature events do not. Heterogeneity analyses suggest that the effect of extreme precipitation events on green technology innovation is more significant for non-state-owned enterprises, poor performance enterprises, and high R&D intensity enterprises than other enterprises. Furthermore, the facilitating effect of extreme precipitation events on green technology innovation is merely temporary.

Reverse mortgage is one of the products (perhaps the main one) that is good to obtain additional income by using the habitual residence as collateral. The main objective of this paper is to analyse the effects that reverse mortgage contracting has on household finances over the lifetime of a family according to the socioeconomic group to which it belongs in Spain. Four indicators are employed to measure the immediate and long-term effects. We use a stochastic model with a double source of randomness, survival and entry into dependency, and apply it to the three socioeconomic groups obtained with cluster methodology from the 2017 Spanish Household Financial Survey data. We conclude that the effects are very different depending on the group: regarding only the effects of hiring a reverse mortgage on the income of the family, widowed women aged between 81 and 85 years, with low income and expenses as well as little net wealth, and a habitual residence that represents half of her net wealth (Cluster 1) are the most benefited; considering that the highest impact indicators are on the probability of illiquidity and on the value of lack of liquidity, the use of reverse mortgages benefits more the families in Cluster 3 (high income and expenses and really high net wealth, head of household aged between 76 and 80 years) and less the families in Cluster 2 (medium income, net wealth and expenses, head of household aged between 65 and 75 years). First published online 17 April 2024

In the swiftly advancing field of Artificial Intelligence (AI), a field where every country aims to keep pace, significant disparities are observed in how different nations adopt AI. This study explores the deep, yet insufficiently studied, effects of AI on societal, economic, and environmental aspects. It particularly examines how brain drain influences governmental AI implementation capabilities, addressing a gap in existing literature. The study investigates the interplay between government AI implementation and brain drain, factoring in macroeconomic conditions, governance quality, educational levels, and R&D efforts. Utilizing 2022 data from European Union countries, the research employs instrumental-variables regressions (2SLS and LIML) to counteract endogeneity and uses clustering methods for categorizing countries based on their government AI levels, alongside spatial analysis to detect cross-national spillovers and interactions. The findings reveal brain drain’s detrimental effect on governmental AI preparedness, highlight clustering tendencies, and identify spatial interdependencies. This paper underscores the need for strategic policy-making and institutional reforms to bolster government AI capabilities. It advocates for a paradigm shift in government frameworks post-New Public Management era, tailored to the new challenges posed by AI. The research, however, is limited to a single year and region, with constraints on data availability and indicator breadth.

The advent of m-commerce has reinvented and simplified the shopping experience for the digital native generation. The following questions were the starting points for this research: is nutrition labeling important in purchase decisions? Could a new format for food nutrition labeling in m-commerce be the optimal way to inform the younger generation and enrich their shopping experience? This study continues the authors’ research on the food preferences of the younger generation by conducting a quantitative study on a sample of 364 students. The aim of the paper is to identify the factors that influence online food orders and the ways in which nutrition labeling can enhance consumers’ purchasing experiences and eating habits. The results show that nutrition facts play an important role in online purchases of new or unfamiliar foods. Control over one’s own diet and a higher income also make digital natives more interested in ordering food online. The use of a mobile format for nutrition labeling would be the necessary update for the food industry to turn nutrition data into added value, help consumers get a balanced diet and personalize nutritional needs, and for policymakers to adjust nutrition standards and policies toward healthier and more responsible consumption patterns.

China’s constantly rapid economic growth accompanying by a low overall inflation has long been mysterious in macroeconomics. The core purpose of this paper is to solve this puzzle. Therefore, we integrate overdetermined set of equations into a MUCSVO model to explore the volatility mechanism of the overall inflation from a sectoral perspective. Our key findings include: 1) the hedging effect of sectoral inflation fluctuations principally accounts for China’s long-run stable overall inflation; 2) the main contradiction of China’s inflation has been shifting from high price levels in the traditional food and residence categories to rising prices in the health care category; 3) as the proportions of inflation in the food and residence categories fall steadily, sectoral inflation weights become more evenly distributed. In conclusion, China’s overall inflation and deflation will be much less likely to occur, while inflation is still of sectoral imbalance. Unusual price fluctuations in the food and health care categories, which are highly relevant to basic living standards of the low-income group, deserve close attention in particular. Overall, besides solving the puzzle of China’s low inflation, our model is applicable to economies that do not publish inflation weights, which is a useful extension of core inflation measurement. First published online 15 March 2024

This paper focuses on the oil and gas sector because of its direct exposure to the complete range of ESG challenges, as well as strong pressure to change business models due to the energy transition. We investigate the ESG scores of a sample of global companies in this sector and their relationship to stock market performance and to the ESG intensity of corporate reports. As an original contribution, we incorporate the intensity of corporate discourse on technology-related sustainability topics for the first time in the literature. Our findings reveal that investors examine both sustainability discourse and results when determining a company’s value and validate the role of ESG scores and rankings in providing investors with an accurate and meaningful assessment of companies’ sustainability actions. Moreover, companies’ disclosure of their sustainable actions and technological developments related to sustainability is positively related to stock returns. This implies that a focus on sustainable practices and constant communication with investors might result in higher market performance. Furthermore, encouraging companies, particularly those in sectors and industries sensitive to ESG factors, to invest in ESG initiatives, is accompanied by improved performance, which makes them more attractive and better positioned to attract financing.

The widespread and substantial effect of the global financial crisis in history underlines the importance of forecasting financial crisis effectively. In this paper, we propose temporal convolutional network (TCN), which based on a convolutional neural network, to construct an early warning system for financial crises. The proposed TCN is compared with logit model and other deep learning models. The Shapley value decomposition is calculated for the interpretability of the early warning system. Experimental results show that the proposed TCN outperforms other models, and the stock price and the real GDP growth have the largest contributions in the crises prediction.

This paper aims to identify the differences in the employment rate dynamics in economy sections, considering gender, in the European Union countries in 2020, compared to the 2008–2019 period. Two methods were used. The first method compares forecasts from models describing employment changes in the pre-pandemic periods with information concerning the actual employment rate in 2020, using three indices measuring the significance of the observed discrepancies. The second method uses dynamic cluster analysis for the 2008–2020 period, and evaluates the changes in composition of groups that occured in 2020. The proposed methods were applied separately to the data concerning the employment of females and males (employment rates) in the EU countries, always divided into economy sections and section groups (A, B-E, F, G-I, J, K, L, M and N, O-Q, R). The application of the “Triple 2 Rule” helped to identify the changes in the previous employment trends (“Interventions”). The evaluation of changes in the dynamics of the employment rate in total and in section groups and according to gender in the EU countries in 2020 – compared to the forecasts from the 2013–2019 trends – revealed that the EU labour market responded differently to the COVID-19 pandemic situation. First published online 14 March 2024

The aim of this paper is to examine the impact of a country’s entry into the monetary union on its economic variables growth rate of real GDP as well as on GDP per capita growth for the period from 2010 to 2020. The clustering method and the ANFIS method were used in the data analysis. A total of two cluster analyses were performed. The first cluster includes countries that joined the EU in 2004 and became EZ members by 2010. The second cluster refers to those countries that joined the EU in 2004 but are not yet members of the EZ. For the first individual cluster analysis two models were analysed and for the second individual cluster three models were analysed using the ANFIS method. As expected, the results showed that GDP growth is connected with trade, inflation and gross investments in fixed capital in the observed countries, while GDP per capita is connected with unemployment, interest rates and public debt. With regard to GDP growth, the difference between countries that are in the eurozone and those that are not is not significant, which is in line with other studies. First published online 14 March 2024

The idea of this research is to combine the influence of personal brand with business needs. This article is a personal brand’s analysis on how personal brand influences consumer interest in products/services. An experiment is carried out on the content viewed by consumers using eye-tracking technology “Neuromarketing & AI LAB” to reveal emotional and structural sensations in the social network Instagram. The aim of this research is to determine the influence of a personal brand on the consumer. Research methodology: analysis of scientific literature, logical definition and theory construction, an experiment: research of human emotions and chosen content. Data analysis method: descriptive statistics and comparative analysis. AOI (heat map) was selected to illustrate the research results. Influencers with up to 100 000 followers were selected – stimuli that trigger or enhance physiological and psychological reactions in the body. It was found that the body and face of a personal brand had the higher influence on consumers. Personal brand attracts consumers’ attention and promotes engagement. The originality and value of the paper: content relevance research of personal brand (identity) influence on consumers in social network Instagram. Further we suggest carrying out similar personal brand’s research choosing other social networks.

This paper focuses on the performance drivers of Foreign Direct Investment (FDI) at the country level, exploring the socio-demographic specifics of donor and receiver countries. To this end, a novel Robust Compromise (RoCo) Multi-Criteria Decision-Making (MCDM) model is proposed using non-linear programming solved by genetic algorithms. The model builds upon established traditional models for alternative ranking and criteria weighting. Subsequently, a stochastic robust regression is performed, building upon previously computed bootstrapped Tobit, Simplex, and Beta regressions to handle performance scores ranging between 0 and 1. The goal is to test FDI performance against a set of contextual variables. The findings suggest that the performance of FDI is relatively low, and relevant improvements should be made. Our second stage analysis reports that higher GDP per capita and good social welfare, including lower infant mortality and higher life expectancy, contribute to the improvement in FDI performance. Furthermore, it is found that a large percentage of women in the total population, wealth concentration in the destination country, as well as the degree of urbanization, are helpful to improve FDI performance. Finally, we find that FDI performance is mainly concentrated on industries that are high-tech and high value-added.

The current situation of the Romanian economy must be understood beginning from the analysis of the main measures of fiscal-budgetary policy applied over the last years by the public authority. In general, the Romanian fiscal policy (before and after accession) was procyclical. However, we continue by presenting some of its characteristics for the past years when we underwent the last ascending phase of the economic cycle. Actually, Romania’s GDP exceeded constantly the potential level, and the demand surplus became predominant, generating inflationary pressures. Maintaining the expansionist level of the fiscal policy, in the conditions of a positive deviation of GDP, as of 2017, and opting-out regarding the structural deficit target contributed to affecting the stability of public finances, on short- and medium-term. Romania entered into an extremely difficult economic context, generated by the pandemic, with an extremely narrow fiscal space which limited a lot the possibilities of combating the effects of the pandemic. In this paper we analyzed a period limited to the year 2020, because we consider this time as marking the end of an economic cycle in a period of peace and economic calm, as another is about to begin based on the new realities. First published online 08 February 2024

Ocean transportation is the most preferred mode of transportation that represents a significant role in the global trade. Ocean transportation comprises around 80% of the aggregate worldwide cargo volume. This research paper focused on evaluating the factors that influence the dwell time of the shipping containers. Dwell time is one of the important port performance parameters which evaluates the time spent by the container in a port. In this research, the data from the fourteen major ports was collected and analysed across the variables, such as cycle, size, mode, status, delivery and tracking technology for evaluating the variation in container dwell time. OLS regression method (Ordinary least squares) along with independent sample T test was adopted for the analysis of 2.8 million container data entries utilizing python for big data analysis and SPSS. For the top three ports with lowest RMSE (Root mean square error), Port A – 15.6 %, Port G – 15.7 % and Port L – 15.86 %, a qualitative study was performed to identify the reasons for the variation in dwell time. The major reasons identified included free days period, trans-shipment port, high rail frequency, industrial hubs in the vicinity of the ports for lower dwell time. A qualitative research framework was presented as the research outcomes and reasons for variations in a multiport study.

Using the policy package pilot implemented in Hubei Province, China, in April 2020 as a natural experiment, we use the synthetic control (SC) and synthetic difference in differences (SDID) methods to estimate the impact of the Chinese government’s support policy on the economic resilience and to analyze the mechanisms by which it impacts. This study finds that the policy package has contributed to the growth of economic resilience in the pilot provinces, with the policy package increasing the average economic resilience of the pilot provinces by 0.062 compared to their potential resilience. The validity and robustness of the above conclusions are objectively confirmed by multidimensional quantitative outcomes such as placebo tests, ranking tests, and replacements in calculating resilience. The mechanism analysis shows that the investment in real estate development, the stimulus for consumption, and the core industry development are virtual channels for the policy package to promote economic resilience growth in the pilot provinces. Moreover, traditional investment in transportation fixed assets plays a minor role. This paper quantitatively corroborates the academic idea that government governance capacity affects regional economic resilience (RER), and research can provide empirical support for regional economic recovery and policy support under a major crisis.