Search

The question of whether the construction of the highway network is economical and can produce positive economic benefits has been a hot topic of discussion in recent years. Previous scholars have explored the impact from multiple perspectives. Our paper draws the “trade potential” model proposed by Armstrong, based on the universal gravity model and the principle of space interaction, which is different from the traffic accessibility, market potential, and market access used in most of the literature. We argue that it is more appropriate to consider both the size impact and the time distance or trade cost impact of the two cities. The paper constructs a conceptual framework and theoretical model for the impact of highways on regional economic growth, measures the “minimum transit time” of highways between prefecture-level cities in China, and calculates the trade potential of prefecture-level cities. Through corresponding empirical model testing, we have obtained some meaningful conclusions. First published online 17 September 2024

The prospective arrival of transformative artificial intelligence (TAI) will be a filter for the human civilization – a threshold beyond which it will either strongly accelerate its growth, or vanish. Historical evidence on technological progress in AI capabilities and economic incentives to pursue it suggest that TAI will most likely be developed in just one to four decades. In contrast, theoretical problems of AI alignment, needed to be solved in order for TAI to be “friendly” towards humans rather than cause our extinction, appear difficult and impossible to solve by mechanically increasing the amount of compute. This means that transformative AI poses an imminent existential risk to the humankind which ought to be urgently addressed. Starting from this premise, this paper provides new economic perspectives on discussions surrounding the issue: whether addressing existential risks is cost effective and fair towards the contemporary poor, whether it constitutes “Pascal’s mugging”, how to quantify risks that have never materialized in the past, how discounting affects our assessment of existential risk, and how to include the prospects of upcoming singularity in economic forecasts. The paper also suggests possible policy actions, such as ramping up public funding on research on existential risks and AI safety, and improving regulation of the AI sector, preferably within a global policy framework. First published online 10 July 2024

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

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.

High-quality innovation is an inevitable requirement for high-quality economic development, and an accurate grasp of the regional distribution characteristics of the level of high-quality innovation development (HQID) can better understand and simulate high-quality innovation. This paper constructs a dataset of high-value inventions based on the Chinese patent database using textual analysis methods and the criteria for High-Quality Inventions proposed by the State Intellectual Property Office, and explores inter-provincial variability and convergence by using the Theil index, convergence, and convergence. This paper finds that High-Quality Inventions are significantly and positively correlated with various indicators measuring the quality and value of patents. In general, the overall level of HQID is on an upward trend with clear differences among provinces, which shows a regional unbalanced but overall rapid development. From the regional differences of HQID, the regional differences of HQID have been narrowed, mainly within the region. In terms of the convergence of HQID, convergence, absolute convergence, conditional convergence, and club convergence exist nationally and within each region, which indicates that the gap between the national and regional levels of HQID has been narrowing over time. HQID has a positive promotion effect on the relationship between HQID and economic growth, economic growth rate, and total factor productivity. High-quality innovation can improve the quality of input factors, cultivate new dynamic energy for economic development, and then promote high-quality economic development. In the context of the shift of IP work from pursuing quantity to improving quality, the scientific understanding of high-quality innovation, the development status, and the differences of regional innovation high-quality development is of great significance for improving innovation quality and efficiency, and effectively supporting high-quality economic development.

This paper analyses the recent evolution of the electricity price of one of the EastEuropean countries’ Balancing Markets (BM) – Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants’ trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players. First published online 14 November 2023

This paper uses the case of Spain to investigate whether and how disruptive technology impacts banking stock returns under a high volatility regime and a low volatility regime. For this purpose, a two-factor model with heteroscedastic Markov switching regimes has been applied. The results indicate that disruptive technologies have an impact on Spanish banking stock returns and that the effects are volatility regime dependent, having a relevant positive impact in high volatility regimes and a less relevant negative impact in low volatility regimes. These findings suggest that investors are informed about and acknowledge the advantages of disruptive technologies and will use their adoption as a business strategy to offset adverse market circumstances. During stable market conditions, on the other hand, Spanish banking seems to have less expectations about disruptive technology as a business strategy. To summarise, this paper provides insights into the role of the pricing of banking-related assets and has other relevant implications for investors that include disruptive technology or banking exposed investments in their portfolios.

The purpose of the paper is to present a methodology for measuring contemporary protectionism – based on data from Poland. In light of the difficulties in assessing all trade barriers, an approximation was proposed: the regional barometer of protectionism (RBP). Recognizing that 21st century protectionism is observable only to a certain extent, the research is based on data that reflect the level of this phenomenon – i.e., media releases as well as Global Trade Alert data. In constructing this barometer, the TOPSIS method (a Technique for Order Preference by Similarity to an Ideal Solution) was used. The RBP constructed (for 2009–2020) is the first regional barometer of protectionism. The results of the research reveal the level and dynamics of protectionism in the case of Poland. This RBP improves the methodology of foreign trade analysis by providing a thorough basis for further research into both the effects and causes of protectionism; this paper presents initial explanations for the latter. Furthermore, after the barometer of protectionism is constructed, trade barriers may eventually be included in further research for models designed to explain Polish trade.

Scanner data mean electronic transaction data that specify product prices and their expenditures obtained from supermarkets’ IT systems by scanning bar codes (i.e. GTIN or SKU). Scanner data are a relatively new and cheap data source for the calculation of the Consumer Price Index (CPI) and the biggest advantage of scanner data is the full product information they provide already at the lowest level of aggregation. Thus, the digitization of the public sector becomes not only something that is needed but an actual necessity resulting from organisational and economic premises (e.g.: reduction of costs or time related to obtaining data). One of main challenges while using scanner data is the choice of the right price index. The list of potential price indices, which could be used in the scanner data case, is quite wide, i.e. bilateral and multilateral indices are used in practice. One of the most important criterion in selecting index formula for scanner data case is the potential reduction of the chain drift bias. The chain drift occurs if the index differs from unity when prices and quantities revert back to their base level. In the paper we present situations on the market leading to the serious chain drift bias. Our main hypothesis is that lagging consumers’ reaction to price changes is the cause of the chain drift effect. Moreover, the article is an attempt to answer the question whether the correlation of prices and quantities may have an influence on the scale and sign of the bias of the measurement of price dynamics. The study focuses also on the scale of over- and underestimation the target full-window multilateral indices by their corresponding splicing extensions. Finally, the paper verifies a hypothesis that the identity test is a key property in reducing chain drift bias. In order to verify the above research problems, both empirical and simulation studies were carried out. Our main result is the confirmation of earlier suspicions that delayed consumer response and price-quantity correlation are determinants of chain drift bias. First published online 06 February 2023

Open innovation (OI) has received significant attention from practices and theories over the past decades. This paper investigates the role of OI and personalized patterns in firms at home and abroad, and then measures the risks involved. Firstly, this paper reviews the definition of OI, the business model innovation characteristics, and the facing problems in practice. Based on the existing literature, we illustrate the openness and challenges of business OI. By introducing bibliometrics, this paper presents a whole research framework. Based on keywords cooccurrence analysis and clustering analysis, we locate the current research hotspots and potential research opportunities from a comprehensive perspective. According to the analysis results, five clusters are obtained, including resource management and value creation; collective innovation and form sustainability; innovation management, intellectual property management, and crossborder cooperation; knowledge management and knowledge sharing; innovation ecosystem, big data, and policy-level innovation. Taking Huawei as an example, its typical business OI model is studied from the perspectives of organizational, project-related, marketing and consumer-based, and summaries the facing challenges and risks. We illustrate its financial performance, innovation performance, and development prospects. We found that, during the implementation of OI practical activities and theoretical exploration, the risks and opportunities facing small and medium-sized enterprises (SMEs) are multiple dimensional.

The main purpose of the paper is to identify the factors that determine the course of green transformation and to assess the current stage and possible directions of change in subsequent years in EU countries. The literature lacks comprehensive studies that show the impact of diverse types of factors on the course of green transformation. For this purpose, two perspectives of the research – economic and environmental – are usually taken into account. This paper also proposes to take social factors into account. The empirical verification was conducted using green growth indicators that are published by the OECD that were compared with factors identified from the literature review. Taxonomic (synthetic) measure of development applying the Weber median and correlation coefficient were used for searching for relationships between the identified groups of indicators described both the changes in the dimension of green growth (internal factors of green transformation) and changes in the dimension of contextual indicators (so-called external factors). The proposed way of combining the results obtained from these two methods of data analysis is also new to research in this field. Its aim is an in-depth exploration of the issue, which enables a 2-step verification of the results obtained. First published online 24 November 2022

Ecological efficiency (EE) provides much reference for formulating appropriate regional economic, social and environmental policies to promote sustainable development. Interactive subsystems of economy, society and environment within EE system have been considered in this paper. By innovatively integrating the merits of two advanced economic research methods (global super efficiency network data envelopment analysis (GSE-NDEA) and panel vector autoregression (PVAR) and updating the EE evaluation indicator system by following the new features of sustainable development in the recent China, this paper comprehensively evaluates EE by drawing evidence from 3 regions in China during the period of 2011–2020, and further reveals how the three subsystems within EE system interact to achieve EE enhancement. The findings show EE and its three subsystems’ trend, the major constrains of EE development, the regional discrepancies in EE progress, and the interactions among the subsystems of economy-society-environment within the EE system in different regions of China. The policy implications are proposed accordingly. First published online 15 November 2022

The perpetual need for developing a sustainable economic environment places the education policies at the foundation of social adaptability. Creating and maintaining curriculum content that meets the demands of a continuously changing society, and the challenges that such a rapid evolution put on the labour market, is one of the top priorities for any education system and institution involved in education at any level. This paper proposes a cognitive computing solution for assessing, in a programmatic manner, large corpora of curriculum content created by teachers from lower secondary education environment for Informatics instruction in Romanian schools. The result of this initiative at the national level is corpora of curricular content that must be evaluated to verify the degree to which the material meets the requirements of the national curriculum. We addressed this crucial yet tedious process by designing and implementing a solution for automating curriculum assessment through cognitive computing. The paper outlines a sustainable framework to evaluate curriculum content in an automated fashion, and for providing critical feedback timely to both content creators, and to policy makers responsible for creating economically viable and future adaptable education strategies. First published online 05 July 2021

In recent years, a series of serious attacks against industrial control system (ICS), such as Stuxnet, Duqu, Flame and Havex, have sounded the alarm for industrial enterprises. For many industrial enterprises, it is a very important part of enterprise management decisions to evaluate ICS security suppliers and choose the appropriate one as a partner. The purpose of this paper is to build the TODIM method based on cumulative prospect theory (CPT-TODIM) to solve multiattribute group decision making problem under interval-valued bipolar fuzzy environment. This extraordinary model not only uses CPT to supplement the traditional TODIM method, but also introduces the entropy weight method to determine the attribute weight so as to avoid the negative influence of subjective weight on the decision result. In addition, in this model, all attribute evaluation information will be interval-valued bipolar fuzzy number (IVBFN) to cope with complex and fuzzy decision environment. Then, the most important part of this paper, we elaborate on the logical structure of the model. What’s more, in the last two sections, we apply this newly constructed model to the selection of ICS security suppliers, and verify the acceptability of this method by sensitivity analysis and comparing with IVBFWA and IVBFWG operator. First published online 05 July 2021

Accurate analysis of technological innovation mechanism in different regions is the key to promoting China’s technological innovation, economic transformation and upgrading. This paper collected statistical data of high-tech enterprises in 27 provinces in China from 2009 to 2016, established a novel PSO-GRNN model, and applied sensitivity analysis to explore the influencing factors and regional differences of enterprise technological innovation in Eastern, Central and Western China. The empirical results showed that the influencing factors were innovation investment, market environment, government support and foreign technology spillover sorting by impact size. Innovation investment was the decisive factor of technological innovation, but innovation resources mainly concentrated on Eastern China, severely insufficient in Central and Western China. Market environment was favorable to Eastern and Central China, but unfavorable to Western China, which restricted greatly the development of Western China. The principalagent problem of state-owned enterprise and the crowding out effect of government research and development funds jointly led to the negative influence of government support on technological innovation. Foreign technology spillover had significant positive effects on technological innovation in Western China. This paper clarifies some disputes about influencing factors of technological innovation and provides a new research perspective for related issues. First published online 27 May 2021

The aim of the paper is to find out whether euro is a convenient substitution for U.S. dollar as an anchor currency for Iranian rial and whether this replacement would affect Iran’s international trade positively. We explore these effects via Optimum Currency Area (OCA) theories using generalized least square from 2000 to 2018. Based on OCA index, euro would be a good substitution for U.S. dollar as an anchor for Iranian rial. In addition, gravity model and Generalized Method of Moments estimation confirm that substitution of U.S. dollar by euro would improve bilateral trade between Iran and its major trade partners especially the European Economic and Monetary Union (EMU). Furthermore, we confirm that a basket containing main currencies (euro, U.S. dollar, yuan, Russian rubble) would be more efficient than a single currency anchor however euro should be prominent in the basket. Such a change of anchor could positively contribute to reduction of transaction costs, diversification of external risk, rise of mutual trade exchanges between Iran and the EMU or the EU and consequent economic growth of trade partners. The paper contributes to the existing literature by comprehensive methodological approach how to identify an appropriate anchor currency. First published online 2 June 2021

Components procurement is a crucial process in supply chain management of the automobile industry. The problem is further complicated by imprecise information and discount policies provided by suppliers. This paper aims to develop a computational approach for assisting automobile components procurement with all-unit quantity discount policy and fuzzy factors, from potential suppliers offering different product portfolios. We propose a two-stage approach consisting of a DEA-TOPSIS (data envelopment analysis procedures followed with a technique for order preference by similarity to an ideal solution) approach for screening suppliers, and subsequentially a fuzzy mixed integer programming (FMIP) model with multiple objectives for optimizing order allocations. The DEA-TOPSIS approach integrates suppliers’ comparative performance and diversity performance into an overall index that improves the ranking of potential suppliers, while the FMIP model features a soft time-window in delivery punctuality and an all-unit quantity discount function in cost. By applying it in a case of automobile components procurement, we show that this two-stage approach effectively supports decision makers in yielding procurement plans for various components offered by many potential suppliers. This paper contributes to integrating multi-attribute decision analysis approach in the form of DEA crossevaluation with TOPSIS and FMIP model for supporting components procurement decisions. First published online 19 November 2020

Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM.

Benchmarking is an effective method for organizations to increase their productivity, quality of products, reliability of processes or services. The organization may make a comparison between its performance and that of the peers from benchmarking, and recognize their advantages as well as disadvantages. The main objective of the present systematic literature review has been the study of DEA benchmarking process. Therefore, it examined and gave a summary of various DEA models applied worldwide to improve benchmarking. Accordingly, a list of published academic papers that appeared in high-ranking journals between 2003 and February 2020 was collected for a systematic review of the DEA benchmarking application. Consequently, the papers selected have been classified according to year of publication, purpose of research, outcomes and results. This study has identified eight major applications including: transportation, service sector, product planning, maintenance, hotel industry, education, distribution and environmental factors. They take up a total of 82% of all application-embedded papers. Among all the applications, the highest recent development has been in both the transportation and service sectors. Results showed higher potential of DEA as a suitable evaluation method for the further benchmarking researches, wherein the production feature between outputs and inputs has been practically lacked or very hard to obtain. First published online 4 January 2021

This paper focuses on calculating resilience index of 282 cities in China from 2012 to 2019, to analysis the regional differences and random convergence. We use the entropy method to calculate the urban resilience index, adopt the Dagum Gini coefficient method to analyze the regional differences and the sources, explore the variation coefficients method and beta convergence model to diagnose the convergence mechanism. The conclusions are: (1) The urban resilience in China is at a medium and low level with a stable growth tendency, with a significant regional unbalance of “higher in east, and lower in other regions”. As the sub-resilience, there is a big gap in the regional difference of the resilience structure with good performance in social resilience and economic resilience, poor in ecological resilience and infrastructure resilience. (2) The Gini coefficient of urban resilience continuously decreases with the regional unbalance narrowing accordingly. The Gini coefficients in different regions have a phased convergence tendency, and the hypervariable density contribution and intra-regional differences contribution are the main sources of differences in urban resilience. (3) The urban resilience in China and eastern region has σ convergence, while China and all regions have significant absolute β and conditional β convergence. Therefore, this paper proposes to continuously accelerate the urban resilient construction, make up for the shortcomings, and narrow the regional development gap, to promote the healthy and orderly development of cities. First published online 19 May 2022

The study examines job satisfaction among workers in four types of organizations (Czech companies, foreign companies, transitory companies, and state institutions) in the Czech Republic. Using Spector’s determinants of job satisfaction, along with innovation and organizational support, a survey was conducted. The analysis involved multi-linear regression, mean, and standard deviation tests. Results show a positive correlation between job satisfaction and factors like communication, nature of work, co-worker relationships, working conditions, contingent rewards, supervision, promotion, and pay across all organizations. We also observed that innovation had a positive correlation with job satisfaction in foreign companies and state institutions, but the impact was not significant. However, there was a significant inverse relationship between innovation and job satisfaction in Czech and transitional companies, though not significant for transitional ones. Organizational support significantly impacted job satisfaction in Czech and transitional companies, but for foreign companies and state institutions, it was not significant. Consequently, the paper suggests that much attention should be given to the area with less satisfied parameters. This research can guide HR, managers, employers, and policymakers in enhancing worker satisfaction based on their abilities or contributions to organizational effectiveness and improving the organization’s economic situation.

Existing research provides limited evidence on the impact of supply chain relationships on the financial performance of upstream and downstream firms. Taking the recent proliferation of supply chain alliances between Chinese listed companies as an opportunity, we study the impact of supply chain alliances on the performance of customers and suppliers as well as the underlying mechanisms at play. We focus on Chinese A-share listed companies from 2001 to 2021 and use announcements issued by the listed companies to identify whether they have established a supply chain alliance with another listed company. The announcements come from the information disclosure system of listed companies of the Shenzhen Stock Exchange and the Shanghai Stock Exchange. Finally, we obtained 148 pairs of supply chain alliances which formed by 231 listed companies and involved 296 announcements of supply chain alliances. By using the propensity score matching method and difference-in-differences regressions, we find that supply chain alliances can alleviate friction between customers and suppliers and significantly improve the efficiency of working capital used by both customers and suppliers. Supply chain alliances increase suppliers’ inventory turnover and accounts receivable turnover, and reduce cash holdings, while supply chain alliances improve customer raw material turnover, and reduce accounts payable turnover. Moreover, the economic impact of supply chain alliances on customers and suppliers is asymmetric. Suppliers benefit more from alliances relative to customers, as evidenced by significant improvements in their financial performance and significantly reduced performance volatility. The “inherent differences” in market power and market value between customers and suppliers can have considerable asymmetries on economic consequences. We divide firms in a pair of supply chain alliances by market power and market value. The magnitude of the financial performance accruing from an alliance is considerably higher for the lower-market-power firm than for the higher-market-power firm. The magnitude of the financial performance accruing from an alliance is considerably higher for the lower-market-value firm than for the higher-market-value firm. Additionally, vertical supply chain alliances can significantly improve firm performance and reduce performance volatility compared to horizontal supply chain alliances. This paper provides new empirical evidence for our insight into the economic consequences of supply chain alliances and their boundary conditions.

With the unstable international environment, the global economy has experienced a slowcession. Previous research on digital innovation in firms has often neglected the impact of macroeconomic cycles. This paper examines the moderating effect of the economic slowcession in the digital transformation and corporate innovation nexus, by using the China’s A-share listed companies’data during 2001 and 2021. The empirical results find that the positive impact of digitization on innovation is countercyclical. During recession, the positive impact of digital transformation on innovation is even greater compared to economic prosperity. Grouped regression results indicate that State-owned listed companies, Non-high tech companies, Large-scale companies, and Eastern companies are more affected by the positive moderating effect of the recession. This indicates that getting out of recession requires more aggressive support of these companies, which promotes innovation and economic recovery. This study provides a useful reference for countries in recession and provides an important complement to traditional economic cycle theory and innovation cycle theory. First published online 15 November 2024

This paper identifies the systemic shocks of total factor productivity (TFP) at the macro level and industry level, and then evaluates the structural stickiness of TFP shocks by using information entropy and industry correlation degree through counterfactual structural simulation based on China’s manufacturing companies. We find that: in the face of TFP systemic shocks, the industries with less structural stickiness include computer communication and other electronic equipment manufacturing, special equipment manufacturing and general equipment manufacturing, indicating that these industries have a strong internal innovation power. The TFP distribution of electrical machinery and equipment manufacturing industry and ferrous metal smelting and rolling industry showed structural differentiation, and the lower tail enterprises are not sensitive to TFP shocks. The industries with strong structural stickiness are non-ferrous metal processing industry and non-metallic mineral products industry, etc., which have weak internal innovation power and need exogenous innovation incentives. In addition, there is a significant positive correlation between industry correlation and information entropy, which emphasizes the radiation effect role of industries with high industry correlation degree. The research provides a new method to evaluate the innovation ability of the industry and a basis for the differentiation of innovation incentive policies in the industry. First published online 15 November 2024

As an essential driving force to promote industrial upgrading and technological innovation, ICT has gradually become the technological support for high-quality economic growth. This paper adopts the improved perpetual inventory method and entropy weight TOPSIS method to measure China’s ICT capital stock and economic high-quality growth index, respectively, and experimentally examines the effect of the former on the latter. The findings show that the south has a more extensive ICT capital stock than the north, and the coastal region has a better high-quality growth index than the inland region. Meanwhile, high-quality economic growth, which has increased since 2009 and is incredibly robust in the south, positively correlates with ICT capital stock. Further research finds that ICT capital stock can boost high-quality economic growth by raising marketization and human capital. The results provide policy recommendations for enhancing high-quality economic growth. First published online 19 November 2024

This paper analyses the impact of digital technological innovation on the carbon emission intensity of enterprises and conducts an empirical test based on the data of listed enterprises in China from 2009 to 2021. The study finds that (1) digital technological innovation can significantly reduce carbon emission intensity. (2) Enterprises’ digital attention and investment can significantly increase their operating income but not reduce carbon emissions. Digital technology patents can significantly reduce carbon emissions in the short term. In the long run, even new digital technologies will have a carbon rebound effect once they are deployed on a large scale. Therefore, digital technology innovation is still challenging in the long run to realize the synergy effect of “increasing production and reducing carbon.” (3) Mechanism tests show that digital technology innovation can reduce carbon intensity by improving operational efficiency, promoting cleaner production, and improving human capital. (4) If the government pays moderate attention to digital development, digital technological innovation by enterprises can significantly reduce carbon intensity. Meanwhile, this effect is more significant in regions with higher levels of intellectual property protection. Digital technology innovation can significantly reduce carbon intensity for mature, high-tech, and technology-intensive enterprises. First published online 18 November 2024

The aim of the paper is to estimate the impact of the rapid worsening of households’ access to credit in Poland on the effects of fiscal policy. The novelty of our study is that it extends the analysis of liquidity constrained households’ impact on fiscal multipliers to Central and Eastern European country, where households access to credit is relatively limited and therefore the potential impact of liquidity constrained households on fiscal multipliers is stronger than the existing literature for highly developed economies indicates. The empirical analysis is based on the theoretical model with heterogenous households. We found that the increase in the percentage of liquidity constrained households led to the substantial rise in fiscal multipliers, and thus the increase of effectiveness of Polish fiscal policy. The study indicates that before the worsening of households’ access to loans, the contemporaneous government spending multiplier was relatively low, whereas after a sharp increase in interest rates, contemporaneous government multiplier exceeded one. What is more, our study shows that a sharp increase in interest rates also strengthens medium term fiscal policy impact on GDP in Central and Eastern European economy. First published online 14 November 2024

Sustainable food supply chain management (SFSC) can control food loss and waste by reducing resource consumption and environmental pollution, thereby ensuring sustainable food consumption and production patterns. Scholars have investigated specific aspects or links in SFSC but rarely studied the sustainability evaluation and selection of a whole supply chain to provide management suggestions under uncertain decision-making environments. This paper presents a comprehensive multiple criteria decision-making method called the SMAA-ORESTE method for SFSC selection. To reduce experts’ efforts, the holistic acceptability index in the SMAA-2 method is used to screen inferior SFSCs from a large number of alternatives. Then, the ORESTE method is combined with the SMAA method to evaluate SFSCs under uncertain information. The ORESTE method can specifically analyze the relationship between alternatives, and the SMAA method can analyze alternatives with unknown criteria weights by Monte Carlo simulation. The proposed method ensures the robustness and credibility of obtained ranking results. An illustrative example validates the applicability and robustness of the proposed method in selecting SFSCs with unknown criteria weights.

In the post-pandemic era, the relative weakness of the economy is reflected not only in the macro data but also in the nighttime economy. Based on the threshold model and unbalanced panel data of 1772 county-level administrative units in China from 2014 to 2020, this paper deeply explores the relationship between digital inclusive finance and night economy and draws the following conclusions. (1) When digital financial inclusion exceeds certain thresholds, its beneficial influence on promoting the nighttime economy increases from 8.13% to 26.59% and 87.71%. (2) Digital financial inclusion promotes the nighttime economy by increasing employment in the secondary industry and promoting technological innovation. However, digital financial inclusion hinders the nighttime economy by reducing entrepreneurial activity in the accommodation and catering sectors. (3) Coverage breadth first inhibits, then promotes, and inhibits the nighttime economy in different intervals; the effect of usage depth and digital level on the nighttime economy in intervals shows an increasing trend. (4) In the Yangtze River Economic Belt region, digital financial inclusion initially inhibits the nighttime economy but turns into promotion as it develops; after the implementation of the rural revitalization strategy in 2017, digital financial inclusion can more positively and significantly promote the nighttime economy.

Our study bridges the gap between in previous research on the synchronization between financial and business cycles over a long period. Using the data for the UK from 1270 to 2016 we analyze the synchronization between financial and business cycles using spectral Granger causality (Breitung & Candelon, 2006). Our paper brings several important findings to the discussion on the financial and business cycle link. Our paper is the first one (to the best of our knowledge) that use data over a long period spanning several centuries. We use spectral analysis and advanced spectral analysis (SSA) and (MSSA) to study the relationship between financial and business cycles in the long run. Paper results show financial and business cycles series moves along over the medium-term spectrum. We find a strong link between the cyclical component in the output (real GDP series) and the cyclical component in the financial series (housing price, credit). First published online 26 May 2020

Preference ordering structures are useful and popular tools to represent experts’ preferences in the decision making process. In the existing preference orderings, they lack the research on the precise relationship between any two adjacent alternatives in the preference orderings, and the decision making methods are unreasonable. To overcome these issues, this paper establishes a novel concept of linguistic preference ordering (LPO) in which the ordering of alternatives and the relationships between two adjacent alternatives should be fused well, and develops two transformation models to transform each LPO into the corresponding double hierarchy linguistic preference relation with complete consistency. Additionally, to fully respect the experts’ expression habits and provide more refined solutions to experts, this paper establishes a multi-stage consensus optimization model by considering the suggested preferences represented in both the continuous scale and the discrete scale, and develops a multi-stage interactive consensus reaching algorithm to deal with multi-expert decision making problem with LPOs. Furthermore, some numerical examples are presented to illustrate the developed methods and models. Finally, some comparative analyses between the proposed methods and models and some existing methods have been made to show the advantages of the proposed methods and models. First published online 24 February 2020

Assessing the sustainability issues of the green energy companies is a multiple criterion decision that includes both quantitative and qualitative elements. The sustainability-balanced scorecard (SBSC) for sustainability evaluation is more difficult than an internal evaluation and it requires more serious investigation in the green energy companies. This paper objectives to mix this sustainability evaluation and use fuzzy information with the SBSC. The study presents a fuzzy DEMATEL (decision-making trial and evaluation laboratory) method to develop an assessment model that integrates triangular fuzzy numbers, and DEMATEL to develop a fuzzy assessment, which prioritizes the relative influence of SBSC for green energy companies’ elements. First, this paper conducts a literature review on SBSC regarding sustainability issues to generate 15 elements with five aspects, which are used to measure the sustainability assessment. Next, fuzzy DEMATEL is employed to manage the uncertain linguistic terms, then to set up an influential network relationship map (INRM). This suggested model provides a structure for the related green energy companies to select the evaluation method and could arrange the sustainability approach according to future competitive pressure. The result shows that environmental aspect is the most direct effect and social aspect is the most important effect aspect to the other aspects. First published online 10 February 2022

In recent years, knowledge management (KM) has consistently attained considerably growing research attention. Consequently, several literature reviews have been performed addressing different topic areas of KM. This paper seeks to present a comprehensive bibliometric and network analysis on KM to understand its development from the perspective of academic communities. Subsequently, it seeks to identify the structure of associations between prior and current themes, predict emerging trends and offer a longitudinal perspective on KM research. This study used web of science database and the initial sample was trimmed down by considering only the articles contributing to KM literature, and further 8,721 KM papers published in the last 30 years were systematically evaluated. The descriptive statistics and science mapping methods employing co-citation analysis were performed with VOSviewer software. In the descriptive analysis, we have analysed publication trends over time, geographical localization of the contributing institutions, journals, most prolific authors, top-performing institutions and most cited articles. Science mapping analysis is based on co-word analysis and co-citations analysis, namely articles’ co-citations and authors’ co-citations. The main findings of this paper will help researchers and academicians to develop knowledge in a specific sub-field by analysing the research outcomes of the papers included in the body of literature. First published online 28 December 2021

This paper develops a duopoly model to analyse capacity sharing strategy and the optimal revenue-sharing contract under a two-part tariff and examines the effects of capacity sharing, cost, and sharing charges in three scenarios. The paper uses the two-part tariff method and adds a more realistic assumption of incremental marginal costs to improve the research on capacity sharing strategies. The results show that capacity constraints affect the sustainable development of firms. A sustainable revenue-sharing contract can create a win-win situation for both firms and promote capacity sharing. Capacity sharing, cost, and the revenue-sharing rate have different impacts in different scenarios; the optimal revenue-sharing rate and fixed fee can be determined to maximise the profits of firms that share capacity. However, capacity sharing may not improve social welfare. First published online 28 December 2021

Poverty is a challenge faced by all countries worldwide. This paper focuses on a factor that has been less well documented: the consumption loss of farmer households caused by the fluctuation of rural public expenditure. Based on large-scale micro household data and climate data, the instrumental variable estimation results show that every 1% fluctuation of rural public expenditure will lead to a 0.113% decrease in farm household consumption. In addition, the fluctuation of rural public expenditure is also a main cause of long-term consumption loss of farmer households. Furthermore, it was found that the impact of rural public expenditure fluctuation on consumption loss is of certain spatial heterogeneity. The worse the spatial environment is, the more serious the consumption loss will be. The policy suggestion of this paper is to ensure a stable scale of rural public expenditure through the central transfer payment, so as to improve the ability of local governments to implement counter cyclical public policies, and transform local finance (industrial investment) into public finance (infrastructure and education) to improve the local space environment. Overall, this study reveals the impact of spatial externality on rural poverty from the perspective of public expenditure fluctuation, and at the same time provides empirical evidence for a better evaluation of the relationship between development and poverty and support for rational regional anti-poverty policies. First published online 08 September 2021

Solvency II imposes risk-based capital requirements on EU insurance companies. This paper evaluates the property risk standard model proposed. The calibration was performed from the IPD UK monthly index total returns for the period between December 1986 and December 2009. In general, it is considered that returns derived from valuation-based indices are smoother than those derived from transaction-based indices. This paper contributes to the existing literature by applying various unsmoothing techniques to this index. The results show that the capital requirements, applying the same calculation method (historical value at risk at the 99.5% confidence level) as in the calibration of the standard model, are generally bigger than those proposed in the standard model of Solvency II.

Sustainable urbanization is significant in developing countries. This paper studies whether Chinese-type sustainable urbanization, that is new-type urbanization, promotes green total factor productivity (GTFP). We find that the new-type urbanization implementation in China, on average, significantly promotes GTFP by 3.2%. The positive effect of new-type urbanization on GTFP is correlated with promoting innovation, especially green innovation, and improving allocation efficiency. We do not find clear evidence for industrial upgrading, including industrial advancement and rationalization. This is because industrial upgrading is a gradual process and cannot be achieved shortly. Instead, we find that pilot cities might screen the entry of new firms and keep polluting firms out comparatively. The heterogeneous results indicate that the promotion effects on GTFP are more salient in regions with strong environmental regulation and adequate factor endowments. We have some practical implications for sustainable development in developing countries. First published online 17 September 2024

Human activities are related to obtain economic, technical, social and environmental benefits. The road construction process participants and road users have direct or indirect benefits from elaboration of new infrastructure. The number of the benefits received by individual entities is publicly discussed by politicians, lobbyists, experts and other decision-makers without having quantitative estimates of benefits, i.e., often relying on intuition or considering theoretical reasoning. The paper suggests a system of 19 benefit entities (criteria) assigned the ranks given by experts. The study involved three categories of experts, including 35 road engineers, 36 transport engineers and 61 road users. The values of the concordance coefficients obtained as a result of the conducted research were found significantly higher than critical values and showed that the opinions of the experts in each category were consistent (not contradictory). This made it possible to consider the average of the opinions of the expert group as a reliable result of solving the problem. Rank averages were replaced by the normalized weights of criteria using Average Rank Transformation into Weight-Linear (ARTIW-L) and – Nonlinear (ARTIW-N) methods. The global averages of criterion weights were used employing the Inverse Hierarchy for Assessment Main Criteria Importance (IHAMCI) method thus calculating the normalized weights of the road-related classified entities (three main criteria). The findings prove that road users benefit the most (weight 0.3485), the road construction contractor (weight 0.3325) is in the next position and the road owner (investor) takes the weight equal to 0.3190. The generated research data can be used for justifying the rationality of road investment.

High-quality marine economic development (HMED) is regarded as a new development pattern of the marine economy in China. This paper aims to examine the dynamic changes and improvement strategies of HMED from the perspective of the green total factor productivity (GTFP) growth. First, the GTFP growth of the marine economy in China’s coastal regions for the period 2007–2020 is calculated using the bootstrapped Malmquist index. Second, the dynamic changes and spatial impacts of the GTFP growth are characterized using kernel density estimation (KDE). Moreover, a novel analytical framework to study the improvement strategies of the GTFP is developed. Within this framework, the fuzzy set qualitative comparative analysis (fsQCA) method is used to explore the paths to achieve HMED. The findings show that: (i) the GTFP growth for coastal regions shows significant fluctuations, suggesting that a stable pattern of marine economic development has yet to be established; (ii) the regional distribution of GTFP growth varies significantly, with provinces with fast GTFP growth gathering resources from neighboring provinces, resulting in a siphon effect; (iii) for coastal provinces that lack certain development conditions, the combined effect of other advantageous factors can be used to achieve HMED. Finally, this study presents policy recommendations for achieving HMED, which can provide insights into the design of China’s future marine economic policies. First published online 10 September 2024

Online lending is a product of digital transformation, which has had a profound impact on the traditional money market. This paper discusses the impact of peer-to-peer (P2P) online lending on the effectiveness of monetary policy. Through the bootstrap sub-sample rolling-window Granger causality tests show that P2P has both positive and negative impacts on the money supply (M2). The positive impact of P2P on M2 indicates that online loans increase the amount of money supply. The negative impact of P2P on M2 shows that it may cut the money supply, thus weakening the monetary policy effectiveness. The general equilibrium model is inconsistent with these results, which underlines a positive effect from P2P to M2. In turn, the negative impact points out that the adjustment of monetary policy will hinder the development of P2P. The negative impact of M2 on P2P indicates that through the regulation of money supply, the online lending market can be correctly guided to prevent financial market from getting out of control. Through the supervision of online lending industry, we can accurately grasp the development of the internet financial industry and reduce its impact on monetary policy. First published online 3 September 2024

The present paper introduces a novel methodology for analyzing decentralization across three dimensions: political, administrative, and fiscal. Considering the challenges in determining the degree of decentralization, we have constructed synthetic measures for assessing political (MPD), administrative (MAD), and fiscal (MFD) decentralization employing the TOPSIS method (Technique for Order Preference by Similarity to an Ideal Solution). The use of this method allowed the creation of a linear rank of OECD countries. Furthermore, on the basis of constructed synthetic measures and utilizing the k-means clustering method, we also constructed a non-linear grouping of those countries. This approach enabled us to achieve the final objective of our research, the revelation of varied interrelationships among the different dimensions of decentralization in selected OECD countries. The combined application of the TOPSIS and the k-means methods enhances the methodology for analyzing decentralization by offering a multidimensional perspective. Given the potential repercussions of negative outcomes, such as overt or covert centralization, the findings of our study could turn out to be of significant relevance. First published online 10 July 2024

The research paper aims to build a composite index of the financial performance of companies, to find if the impact of the COVID-19 crisis was significantly positive for most manufacturing companies listed on Bucharest Stock Exchange, and to look if the manufacturing companies were resilient being prepared with savings that could have mitigate the effects of this pandemic crisis. The results of the FE model selected show that 31.67% of the company’s equity variation is justified by the two independent variables, the stronger correlation of equity being with reserves. Based on the composite index of financial performance built, the manufacturing companies were grouped in three clusters: a cluster with low financial performance companies (z < 4), a cluster with good financial performance companies (4 ≤ z ≤ 8) and a cluster with high financial performance companies (z > 8). The third cluster groups the most analysed companies, on which the pandemic crisis had a positive impact, which achieved the highest financial performance; they are those companies that “take advantage” from the COVID-19 crisis, adapting their business strategy to the market conditions imposed. The article adds value to the specialty literature by building the financial performance’s composite indicator, clustering the manufacturing companies by financial performance’ Z-score.

The main purpose of the paper is to assess the level of EU countries’ ability to compete in a sustainable manner and to compare these results with achievements in the area of sustainable competitive position. In the paper, the definition of the new economic category – sustainable competitiveness and its main components: sustainable competitive capacity and sustainable competitive position, as well as these new terms’ measurement proposal were presented. The study is also an attempt to the exploration of relationship existing between those categories. For this purpose, the selected statistical methods were applied. The taxonomic measure of development as well as the correlation coefficients, were used to measure the multilevel relationship between those considered areas. Until now, those categories were presented only in the context of examining the countries’ ability to compete sustainably. However, the aspects of sustainable competitive position have been completely ignored. That’s why it should be emphasized that this kind of investigation is a novelty in the area of sustainable competitiveness. The research results confirmed the existence of relationship between the various areas that consist to the holistic concept of sustainable competitiveness. These relationships are not always strong though and this is the crucial fact for further research in this area. First published online 1 October 2020

The superiority of BWM over other weighting methods for obtaining the weight values of the attributes is that it achieves high-confidence results with a reasonable number of pairwise comparisons. Although the best-worst method (BWM) is a well-known multi-criteria decision-making (MCDM) method that has been successfully utilized in almost all scientific areas to solve challenging real-life problems, no research has comprehensively examined the state-of-the-art in this regard. The present study depicts a detailed overview of publications concerned with BWM during the period 2015–2022. Based on the information obtained from the Scopus database, this work presents a big picture of current research on BWM. In other words, this paper analyzes the existing literature about BWM and identifies thematic contexts, application areas, emerging trends, and remaining research gaps to shed light on future research agendas aligning with those gaps. Further, the most recent BWM research is analyzed in the top ten scientific areas, from engineering to materials science. “Engineering”, “computer science”, and “business, management, and accounting” are the hottest fields of BWM research. China is the most active country regarding “engineering” and “computer science”, whereas India is the leader in “business, management, and accounting”. The study also reveals that there are still many research gaps in BWM research. The big picture taken in this study will not only showcase the current situation of BWM research but will also positively impact the direction and quality of new research.

Selecting emergency suppliers from a wide range of candidates based on their performance under each criterion can be regarded as a multi-criterion decision making (MCDM) problem. Existing MCDM models to solve the emergency supplier selection problem ignored situations where large-scale suppliers exist, the influence of criteria weights on the robustness of ranking results, and the influence of psychology of regret aversion and disappointment aversion on decision results. To make up for these deficiencies, this paper proposes an MCDM model to solve emergency supplier selection problem with large-scale alternatives. Firstly, to avoid the influence of criteria weights on ranking of alternatives, the Robustness, Correlation, and Standard Deviation (ROCOSD) method is introduced to determine objective weights of criteria based on three objectives. Secondly, the τ-balanced clustering method is applied to cluster large-scale alternatives into balanced clusters. Next, considering the psychology of regret aversion and disappointment aversion of decision makers, a two-stage method is proposed to rank alternatives, which identifies the optimal alternative within each cluster and forms a new cluster consisting of these optimal alternatives in the first stage, and selects the optimal alternative from the new-formed cluster in the second stage. A numerical case is given to validate the proposed model.

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

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.