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This study quantitatively examines the relationship between economic fluctuations and government budget size in the context of China’s fiscal decentralization, drawing inspiration from theoretical predictions of the Keynesian view and empirical studies on other economies.

The panel comprises 31 provinces or equivalents in mainland China, spanning from 1994 to 2019. Diverse estimation strategies including two-way fixed effect regression, the generalized method of moments (GMM) and threshold regressions are, utilized.

The results suggest that under the “tax-assignment system”, neither the central government’s fiscal transfers nor the provincial budgetary revenues or expenditures help reduce economic volatility. Surprisingly, some regression outcomes suggest that government size measures destabilize business cycles.

While the study does not provide supportive evidence for the stabilizing effect of public budgets in Chinese provinces, it promotes a rethinking of the government’s intricate role in macroeconomic stabilization in the context of China’s fiscal decentralization.

This study aims to explore the relationship between financing constraints and the innovation performance of Internet enterprises in the cross-border innovation cooperation network. The study also analyzes the moderating effect of the location of the cross-border innovation cooperation network.

The authors selected patent data, related transaction data and other data of A-share listed companies on Shanghai and Shenzhen stock exchanges from 2014 to 2019. The generalized moment estimation method of instrumental variables (IV-GMM) method was used to analyze the relationship between financing constraints and the innovation performance of Internet firms and the moderating effect of the cross-border innovation cooperation network location. The threshold value of the moderating effect of the network structure hole was calculated with the threshold model.

The empirical results show a significant inverted U-shaped relationship between financing constraints and the innovation performance in the cross-border innovation cooperation network of Internet enterprises. Network centrality positively moderates this relationship. There is a threshold for the adjustment effect of network-structural holes, and the adjustment intensity of structural holes changes before and after the threshold.

This study provides a new perspective for Internet firms in innovation cooperation networks to alleviate the negative impact of financing constraints on innovation performance. The inverted U-shaped relationship between financing constraints and the innovation performance of Internet enterprises is in two stages. The moderating range of network centrality and the structural hole besides the threshold of the moderating effect of a structural hole are detailed.

This study examines the importance of exchange rate and credit growth fluctuations when designing monetary policy in Thailand. To this end, the authors construct a small open economy New Keynesian dynamic stochastic general equilibrium (DSGE) model. The model encompasses several essential characteristics, including incomplete financial markets, incomplete exchange rate pass-through, deviations from the law of one price and a banking sector. The authors consider generalized Taylor rules, in which policymakers adjust policy rates in response to output, inflation, credit growth and exchange rate fluctuations. The marginal likelihoods are then employed to investigate whether the central bank responds to fluctuations in the exchange rate and credit growth.

This study constructs a small open economy DSGE model and then estimates the model using Bayesian methods.

The authors demonstrate that the monetary authority does target exchange rates, whereas there is no evidence in favor of incorporating credit growth into the policy rules. These findings survive various robustness checks. Furthermore, the authors demonstrate that domestic shocks contribute significantly to domestic business cycles. Although the terms of trade shock plays a minor role in business cycles, it explains the most significant proportion of exchange rate fluctuations, followed by the country risk premium shock.

This study is the first attempt at exploring the relevance of exchange rate and credit growth fluctuations when designing monetary policy in Thailand.

The purpose of this paper is to study the influence of inflated energy prices on the capital structure of Indian manufacturing corporations and to investigate whether the capital structure of Indian firms is driven by demand shocks or supply shocks during the study period.

After conducting a thorough review of the capital structure and inflation-based research studies, panel data-based regression model and correlation matrix have been used as statistical tools for Indian manufacturing sector available with the Centre for Monitoring Indian Economy Prowess database.

The results suggest that variables like the presence of inflated energy prices had adversely influenced the capital structure of Indian corporations. Not only this, the study also highlights that factors pertaining to the demand shock had induced Indian corporations to have higher debt levels in the capital structure.

This study has laid some ground work to explore the influence of inflation on capital structure of Indian firms upon which a more detailed evaluation could be based.

To the best of the authors’ knowledge, this study is the first that explores the influence of inflated energy prices on the capital structure of manufacturing firms in India by using the most recent data.

This article offers a review of the literature on regional business cycles (BCs) in emerging economies. The objective is synthesizing the existing studies based on theoretical, empirical and methodological approaches.

The methodological framework includes the following stages: research questions, bibliography location, the selection of articles and the evaluation of the literature, analysis and synthesis, and the reporting and use of results.

The evidence suggests that expansionary phases last longer than recessions'; public expenditure shows a pro-cyclical behavior; and factors such as productive structure and international trade explain the synchronization of regional BCs.

Up until now, there is no research that performs a review of regional BCs in emerging economics.

The purpose of this study is to investigate the significance and impact of strategic knowledge management (SKM) practices and organisational change capacity (OCC) in improving strategic thinking and strategic orientation in small and medium-sized enterprises (SMEs) and their contribution in overall improvement of entrepreneurial performance.

Quantitative research methodology using partial least square structural equation modelling with data of 296 sample from the target group as managers and owners from various SMEs in the UK has been used in the study.

The findings suggest that SMEs that invest in SKM and OCC are more proficient at adjusting to fluctuations in the business landscape and develop effective strategies that lead to improved entrepreneurial performance. The study provides evidence that SKM encompasses more than just the acquisition and use of information. It also involves the establishment of a learning and innovation culture that facilitates strategic thinking and direction. Similarly, OCC is not just about implementing change but also about developing the agility and flexibility to adapt to market changes, consumer demands and technology.

According to the research, SMEs may boost their entrepreneurial performance and keep a competitive advantage in the modern, dynamic business environment by investing in SKM and OCC. The capacity of SMEs to implement SKM and organisational change should be encouraged and supported by policymakers and practitioners, who should also offer the necessary tools and assistance to do so.

This study offers a valuable addition to the previously published works on SKM and OCC within SMEs. It offers empirical data that highlights the significance of SKM and OCC in fostering strategic thinking, strategic orientation and ultimately, boosting entrepreneurial performance. The study also highlights the challenges faced by SMEs in implementing SKM and OCC and provides recommendations for overcoming these challenges.

This study investigates the nexus between the returns on oil prices (OP) and unemployment (UR) while taking into account the influences of two of the most representative measures of uncertainty, the Baker et al. (2016) and Caldara and Iacovello (2021) indexes of economic policy uncertainty (EP) and geopolitical risks (GP), in the relationship.

The authors use data on the US, Canada, France, Italy, Germany and Japan from January 2000 to February 2022 and the UK from January 2000 to December 2021. The authors then apply the continuous wavelet transform (CWT), wavelet coherence (WC), partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) to examine the returns within a time and frequency framework.

The CWT tracks the movement and evolution of individual return series with evidence of high variances and heterogenous tendencies across frequencies that also align with critical events such as the GFC and COVID-19 pandemic. The WC reveals the presence of a bidirectional relationship between OP and UR across economies, showing that the two variables affect each other. The authors’ findings establish the predictive influence of oil price on unemployment in line with theory and also show that the variation in UR can impact the economy and alter the dynamics of OP. The authors employ the PWC and MWC to capture the impact of uncertainty indexes in the co-movement of oil price and unemployment in line with the theory of “investment under uncertainty”. Taking into account the common effects of EP and GP, PWC finds that uncertainty measures significantly drive the co-movement of oil prices and unemployment. This result is robust when the authors control for the influence of economic activity (proxied by the GDP) in the co-movement. Furthermore, the MWC reveals the combined intensity, strength and significance of both oil prices and the uncertainty measures in predicting unemployment across countries.

This study investigates the relationship between oil prices, uncertainty measures and unemployment under a time and frequency approach.

1. Wavelet approaches are used to examine the relationship between oil prices and unemployment in the G7.

2. We account for uncertainty measures in the dynamics of oil prices and unemployment.

3. We observe a bidirectional relationship between oil prices and unemployment.

4. Uncertainty measures significantly drive oil prices and unemployment co-movement.

5. Both oil prices and uncertainty measures significantly drive unemployment.

Wavelet approaches are used to examine the relationship between oil prices and unemployment in the G7.

We account for uncertainty measures in the dynamics of oil prices and unemployment.

We observe a bidirectional relationship between oil prices and unemployment.

Uncertainty measures significantly drive oil prices and unemployment co-movement.

Both oil prices and uncertainty measures significantly drive unemployment.

This research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.

The architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.

The experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.

The architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss.

The ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.

This paper aims to investigate the stationary properties of entrepreneurship capital in the Iranian business sector. The investigation is conducted based on firm size (micro, small and medium, and large), sector (ISIC classification), and location (23 provinces) over the period 1981–2021.

To achieve the purpose of the paper, we apply the Lagrange Multiplier (LM) unit root test with structural breaks.

The results of our study confirm the stationarity of entrepreneurship for the majority of our categorizations. Therefore, entrepreneurs' willingness to start a business is temporarily affected by sharp shocks, suggesting that the entrepreneurship trends in these categories will eventually revert to their long-run equilibrium. However, the time series of entrepreneurship in provinces such as Zanjan and Lorestan, as well as in the electrical machines and devices (code 31) sector, remain permanent. These findings can assist policymakers in each sector and location in designing effective policies to promote entrepreneurial activities.

In this study, the time period utilized was restricted, and there were no data accessible for an extended duration. Another limitation of this research is the absence of access to firm-level data on a shorter time scale, such as weekly or monthly.

The economic literature reveals that empirical studies on the persistence of entrepreneurship have received relatively less attention in the context of emerging and resource-based economies, compared to the increasing focus on them in developed countries. Therefore, to address this gap, this paper aims to extend the current empirical literature by presenting new evidence for the case of Iran, which has an emerging and resource-based economy.

This study's main goal is to explore how small and medium-sized enterprises (SMEs) in the food as well as beverage (F&B) area should evolve to survive in a bustled post-pandemic future.

Drawing on the knowledge-practice gap in the field of Entrepreneurial Orientation (EO) as well as Business Model Innovation (BMI), this study examines an integrated framework using the F&B sector as an empirical field of research. This study uses an empirical quantitative approach using the main information aggregated from a questionnaire administered during the study. The sample had 467 valid responses (71.08% response rate). This paper uses the Partial-Least Squares Structural Equation Model (PLS-SEM) to examine structural relationships because this technique is usually proposed for models with complex relations.

According to the research EO is directly and indirectly, correlated with BMI. The correlation is positive and significant. Simultaneously, corporate social responsibility (CSR) is partially mediated in the stated relation between EO as well as BMI, and CETA where it shows a positive moderation part in EO's influence on CSR. In particular, it elucidates how EO promotes CSR and BMI, alongside moderating part of circular economy principles adoption (CEPA).

These research findings suggest that SMEs in the F&B sector should transform the way they traditionally manage their businesses by applying circular economic principles into entrepreneurial orientation to promote CSR-based practices and acquire the resources obtained from internal and external stakeholders. Hence, their business can recover from post-pandemic effects and become resilient to future disruptions. Furthermore, this study suggests that adopting circular economy principles provides a key source of knowledge and insights that allows firms to sharpen their entrepreneurial orientation toward incorporating stakeholders and social environmental concerns into business models for sustainability, especially in the uncertain and drastic changing environment.

This study proposes a novel approach by which BMI is formed and evolved based on circular economy principles-oriented entrepreneurship and the synergies of stakeholders through undertaking corporate social responsibility practices. Drawing from the knowledge-practice gap, this research adds to the existing knowledge about EO as well as BMI with a conceptualized and operationalized empirical framework in a novel context that was not covered in current literature, especially in the context of uncertainties and disruptions experienced during COVID-19 outbreaks. The F&B sector was hit and how it should evolve to recover and build resilience is perceived as an urgent issue.