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This study investigates the impact of US dollar volatility and oil rents on the performance of small and medium-sized enterprises (SMEs) in the Gulf Cooperation Council (GCC) region, with an emphasis on understanding how these factors influence SME financing constraints in economies with fixed currency regimes.

Employing a random effects panel regression analysis, this research considers US dollar volatility and oil rents as independent variables, with SME performance, measured through the financing gap, as the dependent variable. Controls such as trade balance, inflation deltas and gross domestic product (GDP) growth are included to isolate their effects on SME financing constraints.

The study reveals a significant positive relationship between dollar volatility and the financing gap, suggesting that increased volatility can exacerbate SME financing constraints. Conversely, oil rents did not show a significant direct influence on SME performance. The trade balance and inflation deltas were found to have significant effects, highlighting the multifaceted nature of economic variables affecting SMEs.

The study acknowledges potential biases due to omitted variables and the limitations inherent in the use of secondary data.

Findings offer pertinent guidance for SMEs and policymakers in the GCC region seeking to develop strategies that mitigate the impact of currency volatility and support SME financing.

The research provides new insights into the dynamics of SME performance within fixed currency regimes, which significantly contributes to the limited literature in this area. The paper further underscores the complex connections between global economic factors and SME financial health.

The study aims to find out the impact of financial inclusion and financial development on financial stability using panel data from eight countries in the Middle East and North Africa (MENA).

To achieve the aim of the study, the researcher prepared two indicators of financial inclusion and governance to find out the impact of financial development on the relationship between financial inclusion and financial stability. Data on financial inclusion was obtained from the International Monetary Fund, data on financial development and financial stability were obtained from the World Bank.

The results of the fixed and random effect methods show that financial inclusion has a significant positive effect on financial stability. Additionally, financial development represents a moderating variable in the significant positive effect on the relationship between financial inclusion and stability in the MENA countries.

The current study suffers from some limitations that researchers must be aware of in future research. First, there is an inability to determine qualitative aspects such as time and cost when designing a composite indicator of financial inclusion. Second, due to limited data, we used only eight countries from the MENA. It is suggested to expand the sample to include other countries.

This paper contributes to the related literature between financial inclusion and financial stability by confirming or denying the results of previous studies. Also, to the best of the author’s knowledge, this paper is the only one that explains the role of financial development in the relationship between financial inclusion and stability in MENA countries, using a composite index to calculate financial inclusion. Finally, the study seeks to focus the attention of the government and policymakers to build a system of financial inclusion that leads to improving financial stability.

This study proposes an analysis of the performance of companies that have assumed the responsibility of facilitating the digitalization of economic activities. Because of their potential to accelerate digitization, these companies have been financially supported. The monitoring of the performances recorded by these companies, including the evaluation of the impact of different determining factors, meets both the needs of the financiers (concerned with the evaluation of the efficiency of the use of nonreimbursable financing) and the needs of continuous improvement of the activities of the companies in the field.

The study assesses performance dynamics and the impact of its determinants. The model allows achieving a simplified vision of performance and its determinants, supporting decision-makers in the management process. The construction of an estimation model based on the multiple regression method was considered. Robustness tests were performed on the results, using parametric and nonparametric tests.

The results of the analysis at the level of the extended sample indicated that, during the analyzed period, the economic and commercial performances decreased, and significant influences in this respect include the financing structure, sales dynamics and volume of receivables. The analysis at the level of the restricted sample confirmed these interdependencies and provided additional evidence of the impact of other determinants.

The study contributes both to performance research and to the assessment of the prospects for accelerating digitalization in support of economic activities. Since the empirical research was carried out on a sample of Romanian companies that provide services in information technology, which accessed nonreimbursable financing, the representativeness of the results is limited to this sector. For the analyzed sample, the study provides support for improving performance.

The results of the study prove to be useful from a microeconomic and macroeconomic perspective as well, as they provide evidence on the performance of companies that have implemented information and communication technology (ICT) projects and on the efficiency of the use of non-reimbursable funding dedicated to business support.

The study fills the literature gap regarding the performance of companies that have developed ICT projects and received grant funding for the implementation of these projects. The literature review indicated that there are few studies conducted on these companies, which did not include Romanian companies.

This study aims to revisit the empirical debate about the asymmetric relationship between oil prices, energy consumption, CO₂ emissions and economic growth in a panel of 184 countries from 1981 to 2020.

A relatively new research method, the PVAR system GMM, is applied.

The outcome of the PVAR system GMM model at the group level in the study suggests that oil prices exert a positive but statistically insignificant effect on economic growth. Energy consumption is inversely related to economic growth but statistically significant, and the correlation between CO₂ emissions and economic growth is negative but statistically insignificant. The Granger causality test indicates that oil prices, CO₂ emissions, oil rents, energy consumption and savings jointly Granger-cause economic growth. A unidirectional causality runs from energy consumption, savings and economic growth to oil prices. At countries’ income grouping levels, oil prices, oil rent, CO₂ emissions, energy consumption and savings jointly Granger-cause economic growth for the high-income and upper-middle-income countries groups only, while those variables did not jointly Granger-cause economic growth for the low-income and lower-middle-income countries groups. The modulus emanating from the eigenvalue stability condition with the roots of the companion matrix indicates that the model is stable. The results support the asymmetric impacts of oil prices on economic growth and aid policy formulation, particularly the cross-country disparities regarding the nexus between oil prices and growth.

From a methodological perspective, to the best of the author’s knowledge, the study is the first attempt to use the PVAR system GMM and such a large sample group of 184 economies in the post-COVID-19 era to examine the impacts of oil prices on countries’ growth while controlling for other crucial variables, which is noteworthy. Two, using the World Bank categorisation of countries according to income groups, the study adds another layer of contribution to the literature by decomposing the 184 sample economies into four income groups: high-income, low-income, upper-middle-income and lower-middle-income groups to investigate the potential for asymmetric effects of oil prices on growth, the first of its kind in the post-COVID-19 period.

The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

This study aims to understand client food preferences and how pantry offerings can be optimized by those preferences.

This study develops and administers customized surveys to study three food pantries within the Second Harvest Food Bank of Northwestern North Carolina network. This study then categorizes food items by client preferences, identifies the key predictors of those preferences and obtains preference scores by fitting the data to a predictive model. The preference scores are subsequently used in an optimization model that suggests an ideal mix of food items to stock based upon client preferences and the item and weight limits imposed by the pantry.

This study found that food pantry clients prefer fresh and frozen foods over shelf-friendly options and that gender, age and religion were the primary predictors. The optimization model incorporates these preferences, yielding an optimal stocking strategy for the pantry.

This research is based on a specific food bank network, and therefore, the client preferences may not be generalizable to other food banks. However, the framework and corresponding optimization model is generalizable to other food aid supply chains.

This study provides insights for food pantry managers to make informed decisions about stocking the pantry shelves based on the client’s preferences.

An emerging topic within the humanitarian food aid community is better matching of food availability with food that is desired in a way that minimizes food waste. This is achieved by providing more choice to food pantry users. This work shows how pantries can incorporate client preferences in inventory stocking decisions.

This study contributes to the literature on food pantry operations by providing a novel decision support system for pantry managers to aid in stocking their shelves according to client preferences.

The COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of geopolitical risk (GPR) and other indices on TA over 1995–2023.

We employ a nonlinear autoregressive distributed lag (NARDL) model to analyze the effects, capturing both the positive and negative shocks of these variables on TA.

Our research demonstrates that the NARDL model is more effective in elucidating the complex dynamics between macroeconomic factors and TA. Both an increase and a decrease in GPR lead to an increase in TA. A 1% negative shock in GPR leads to an increase in TA by 1.68%, whereas a 1% positive shock in GPR also leads to an increase in TA by 0.5%. In other words, despite the increase in GPR, the number of tourists coming to Russia increases by 0.5% for every 1% increase in that risk. Several explanations could account for this phenomenon: (1) risk-tolerant tourists: some tourists might be less sensitive to GPR or they might find the associated risks acceptable; (2) economic incentives: increased risk might lead to a depreciation in the local currency and lower costs, making travel to Russia more affordable for international tourists; (3) niche tourism: some tourists might be attracted to destinations experiencing turmoil, either for the thrill or to gain firsthand experience of the situation; (4) lagged effects: there might be a time lag between the increase in risk and the actual impact on tourist behavior, meaning the effects might be observed differently over a longer period.

Our study, employing the NARDL model and utilizing a dataset spanning from 1995 to 2023, investigates the impact of GPR, gross domestic product (GDP), real effective exchange rate (REER) and economic policy uncertainty (EPU) on TA in Russia. This research is unique because the dataset was compiled by the authors. The results show a complex relationship between GPR and TA, indicating that factors influencing TA can be multifaceted and not always intuitive.

The purpose of this study is to explore the determinants influencing industrial adoption of green hydrogen amidst the global transition towards sustainability. Recognizing green hydrogen as a pivotal clean energy alternative for industrial applications is critical for understanding its potential integration into sustainable practices.

This research examines the impact of factors such as innovativeness, perceived ease of use, user comfort, optimism and governmental policies on the industrial intention towards green hydrogen usage. Using responses from 227 Indian industry professionals and conducting analysis via the SmartPLS software, the study reveals a discernible discomfort among industrial workers pertaining to the daily application of green hydrogen.

The research presents an array of policy recommendations for stakeholders. Emphasized strategies include the introduction of green hydrogen certificates, sustainable public procurement mechanisms, tax incentives, green labelling protocols and the establishment of a dedicated hydrogen skill development council, all of which can significantly influence the trajectory of green hydrogen adoption within the industrial sector.

This research synthesizes various elements, from industry perception and challenges to policy implications, presenting a holistic view of green hydrogen’s potential role in industry decarbonization and SDG realization. In essence, this study deepens not only the empirical understanding but also pioneers fresh theoretical frameworks, setting a precedent for subsequent academic endeavours.

The lean startup approach (LSA) is extensively utilized by early-stage entrepreneurs, with “pivot” serving as a key pillar. However, there is a research gap concerning the boundary conditions impacting LSA and pivot decisions, especially when addressing societal challenges, as in the context of transformational entrepreneurship. In this regard, the healthcare sector, further compounded by a lack of research on startups and scale-ups, presents an embraced opportunity to provide multiple contributions for both theory and practice.

The present investigation employs a grounded approach to explore the experiences of the co-founders of a fast-growing Italian e-health startup. A narrative strategy was employed to organize conditions and evolving strategic action/interactions into three different pivoting phases of the startup – before the pivot, its enactment and aftermath – with primary and secondary data collected over a period of one year.

Pivoting in digital healthcare unfolded as a liminal experience marked by factors such as high regulation, multiple stakeholders, technological and symbolic ambivalence, resource-intensive demands and institutional actors acting as pathway pioneers, leading to an information overload and unforeseeable uncertainty to manage. These factors challenge entrepreneurs' ability to attain optimal distinctiveness, presenting the paradoxical need for vertical flexibility for scaling up.

By uniquely illuminating the sector’s constraints on entrepreneurial phenomena, this study provides a valuable guide for entrepreneurs and institutional actors in addressing societal challenges.

This study introduces a process model of transformational information crafting when pivoting, highlighting the role of entrepreneurs' transformational stance and platform-mediated solutions as engines behind strategies involving information breaking and transition, preceding knowledge-driven integration strategies.

Liquidity is a primary concern for businesses. The purpose of this study is to understand the impact of the collaborative liquidity management within the supply chain. Larger firms prescribe favorable trade terms in the transactions and do not engage in value chain vision sharing with their smaller counterparts. Smaller firms encounter challenges with liquidity and often face the risk of bankruptcy. Such practice can threaten the entire supply chain. Instead, collaborative liquidity management can offer a win–win scenario to both parties. In that case, what are the benefits of implementing a collaborative liquidity management approach across the value chain, and what is the reward?

The authors selected key liquidity metrics that matter most to the organizations from a cohort of 307 firms from the Indian automobile industry for 10 years (2012–2021). The authors classified the businesses into five distinct revenue-based categories. They emphasized the importance of expanded supply chain finance adoption and demonstrated how collaborative liquidity management strategies boosted return on assets.

The research confirms the tangible benefits of greater adoption of supply chain finance in realizing supply chain members’ shared vision. The authors challenged the age-old practice of power-based relationships in the supply chain. They recommended a win–win scenario through practical cooperation and increased adoption of SCF by value chain members.

Existing research predominantly focuses on dyadic relationships and is restricted to Europe and China. According to the authors, no comprehensive investigation has been conducted in India. This application of simulation techniques to improve the liquidity performance of companies in developing economies is innovative.