This literature review explores the impact of exchange rate (FX) volatility on the cash flows of non-financial firms within the MENA region, a subject of growing importance due to the region’s economic openness, reliance on oil exports, and widespread currency pegs to the US dollar. The purpose of this study is to synthesize and critically assess prior research to understand the financial vulnerabilities and risk management practices of MENA firms exposed to foreign currency fluctuations. Drawing upon a wide range of peer-reviewed empirical and conceptual studies published between 2000 and 2025, including some theories from 1970, this review integrates evidence from global and regional contexts, with particular attention to the effects of the COVID-19 pandemic on FX exposure. A narrative literature review (NLR) methodology was employed, utilizing thematic analysis to extract and compare findings across diverse sources, while evaluating methodological strengths and gaps in the existing body of work. The findings highlight the high sensitivity of MENA non-financial firms’ cash flows to exchange rate movements, limited hedging practices, and structural constraints such as shallow derivative markets. The review concludes with recommendations for enhanced financial disclosure, derivative market development, and FX policy reform, alongside a call for more firm-level, post-pandemic empirical research in the region.

Through empirical analysis, the article uses panel analysis to assess the impact of macroeconomic indicators on the probability of death from non-communicable diseases (NCDs) across two groups of countries with differing GDP per capita (Azerbaijan-Turkey and Israel-Austria). The panel analyses were conducted using OLS (Ordinary Least Squares) and GMM (Generalized Method of Moments). The findings indicate that the Human Development Index (HDI) is the key determinant influencing the probability of death from NCDs. As HDI increases, the likelihood of death from NCDs decreases. The panel analysis, covering countries with significantly varying levels of healthcare expenditures, reveals that an increase in healthcare expenditures does not reduce the probability of death from NCDs but, on the contrary, increases it. This outcome can be attributed to two main factors. Firstly, higher healthcare expenditures may improve the identification and reporting of NCD-related deaths, thereby increasing the observed probability. Secondly, healthcare expenditures may be utilized inefficiently, leading to less effective outcomes in reducing deaths caused by NCDs.

The transition to sustainable tourism in the European Union (EU) is increasingly shaped by green and digital transformation. However, the efficient use of local natural, cultural, human, and technological resources remains vital for enhancing sustainability and improving quality of life. This article examines how smart tourism destinations can operationalize sustainability through data-driven tools, stakeholder collaboration, and territorial assets. Using the SMART Sustainability Framework, it analyzes key indicators such as tourism intensity, digitalization levels, and green initiative adoption, with a focus on newer EU Member States. Case studies from Ljubljana, Gothenburg, Valencia, and Madeira illustrate varied approaches to integrating local resources via ICT, participatory governance, and sustainable mobility. Comparative analysis highlights significant disparities, with High Performers like Slovenia and Spain aligning digital innovation with local sustainability goals. The article concludes with policy recommendations to strengthen local capacities, scale digital tools, and promote place-based strategies for more inclusive and resilient tourism development.

This research investigates the growing research of AI in marketing within Asian countries, which are at the forefront of AI technology adoption. Despite the global proliferation of AI marketing studies, a comprehensive bibliometric analysis targeting Asian countries is lacking. This study aims to fill this gap by synthesizing research trends, influential contributions, and future directions of AI in marketing within this region. Utilizing the Scopus database, 1,909 articles were analyzed through bibliometric techniques using VOSviewer to visualize research trends and relationships. The analysis identifies leading researchers, key journals, affiliations, countries, and prevalent themes in AI marketing research in Asia. The study reveals a significant increase in publications post-2010, peaking around 2020, reflecting technological advancements and industry adoption of AI. However, a decline post-2025. Emerging contributors like Pakistan and Turkey are showing growing interest and potential in AI marketing research. Continued exploration of new AI technologies such as augmented reality, the metaverse, and virtual reality for marketing purposes. The findings underscore the necessity for ongoing research to address emerging challenges and opportunities in AI marketing. This research provides valuable insights for academics, practitioners, and policymakers, highlighting the critical role of AI in shaping the future of marketing in Asian countries.

This study focuses on analyzing interest rate dynamics trends in different economies. First, we derive the marginal distribution of the threshold Ornstein-Uhlenbeck (OU) process. Then, we explicitly construct its probability density function (PDF). Additionally, we use the maximum likelihood estimation (MLE) method to estimate the model parameters, as it is statistically robust and ensures reliable results. Afterwards, we utilize historical interest rate data from four countries: China, the United States, the United Kingdom and Switzerland to validate the model parameter estimates. We've figured out that the threshold OU model is better at capturing the nonlinear dynamics in interest rates. Consequently, the model can furnish a reference basis for monetary policy formulation and financial decision-making in a variable macroeconomic environment.

This research aims to examine the influence of Learning Organizations and achievements on Micro, Small, and Medium Enterprises (MSMEs) in macroeconomic terms in Indonesia. Using macro data indicators such as Gross Domestic Product (GDP) Growth, Gross Fixed Capital Formation Growth, Export Growth, Labor Absorption Growth, and Entrepreneurship Ratio, this research uses secondary data from the World Bank with observation years 2006 to 2023. The method used is a vector autoregression model. The research results show that the key factors in the learning organization and achievement of MSMEs lie in labor absorption and the entrepreneurial ratio. The response to GDP growth shows fluctuations in economic growth, which are influenced by other factors. The link between employment and GDP growth needs to be considered in economic policy. Policies that encourage entrepreneurship can strengthen business sector growth, and changes in exports also affect GDP growth. Investment in Gross Fixed Capital affects long-term growth.

Digital transformation processes are having a significant impact on the business sector of Azerbaijan, creating new opportunities for the development of small and medium-sized businesses (SMEs), in particular. Automation is often associated with innovation, efficiency, and competitiveness, which are essential elements for the growth of SMEs in a developing economy. The study aimed to examine the technological, market, and cultural factors influencing the tendencies of SMEs to switch to automated management. The analysis of the tendencies of SMEs to switch to automated management in Azerbaijan is relevant due to its potential to contribute to economic development, reveal cross-cultural dynamics, and increase business adaptability. The article also examines the main problems of small and medium-sized businesses in the implementation of automation systems, such as cybersecurity, technical integration, and bureaucratic obstacles. In addition, the article explores how automation helps employees focus on more valuable activities, simplifying their work processes. The importance of increasing investments in training programs, strengthening collaboration with automation vendors, and developing risk management strategies to enhance the potential of SMEs in the field of digitalization was highlighted. The main purpose of the study is to study and gain a deep understanding of the factors influencing the transition of small and medium-sized businesses (SMEs) to automated management systems in Azerbaijan. This study, which analyzes the nuances of technology adoption, cultural influences, political factors, and market competitiveness, aims to provide valuable suggestions and recommendations to policymakers, businesses, and stakeholders investing in the development and sustainability of small and medium-sized businesses.

This paper proposes and empirically validates an integrated model within the public service sector, examining the mediating effects of public-organization relationship quality and citizen well-being on the relationship between public service quality and organizational recommendation. To assess the model's validity, a survey was conducted with 874 citizens enrolled in the Greek Social Insurance and Pensions System. Data was analyzed using Partial Least Squares (PLS) path modeling. The results confirm the mediating role of public-organization relationship quality and citizen well-being in the relationship between service quality and organizational recommendation. The findings suggest that public organizations delivering high-quality public services foster stronger, higher-quality relationships with citizens, enhance their well-being, and consequently receive more positive recommendations. The study concludes with a discussion of theoretical and managerial implications, limitations, and directions for future research.

Quality control of products and services is essential across various industries and sectors, including manufacturing, engineering, public health, economics, finance, and medicine. It is critical to ensure that product characteristics align with customer requirements while maintaining consistent quality and standards. Control charts are essential tools in Statistical Process Control (SPC), used to detect changes in process means or variability, and play a critical role in monitoring, controlling, and enhancing quality within production processes. This study evaluates the Average Run Length (ARL) of the Double Homogeneously Weighted Moving Average (DHWMA) control chart for Moving Average processes (MA(q)) with exponential white noise, using Numerical Integral Equation (NIE) methods. These methods include Simpson’s Rule, the Midpoint Rule, the Trapezoidal Rule, and the Gauss–Legendre Quadrature. Additionally, the study compares the performance of the DHWMA and HWMA control charts. The results indicate that all four NIE methods yield similar ARL values. However, CPU time analysis shows that the Midpoint Rule generally requires less computation time than the other methods. Furthermore, when evaluating change-point detection effectiveness, the DHWMA control chart consistently outperforms the HWMA chart across all shift sizes, as demonstrated by its lower Expected Average Run Length (EARL).

This paper aims to describe some of the methodological challenges and concerns that come with the changeover from the NACE Rev.1.1 to the NACE Rev.2 in business statistics, illustrated by the SBS case in Albania. Different sampling and estimation strategies must be considered to produce reliable figures for the domains under both classifications simultaneously. Implementing the revised classification of economic activities, NACE Rev.2, is a major task for Albania and countries that haven’t implemented it yet. A substantial amount of work will need to be carried out in the range of business registers by double coding the activities in both NACEs, business survey methodology, and national accounts. On the other hand, this article discusses that the change of NACE provides an excellent opportunity to substantially improve sample and estimation design and build bridges between old and new NACE. In such situations, the micro and macro approaches should be carefully analyzed among themselves, with their pros and cons, and the appropriate decision for back-casting purposes should be considered. During this process, the National Statistical Institute will face several major challenges, that are illustrated with examples while elaborating the idea behind this paper. Through the examples, this article highlights the roadmap to the back-casting procedure. It is a cornerstone for the next implementation of NACE Rev.2.1, in Albania and the Institute of Statistics staff', and it also comes in handy for anyone who wants to get to know the back-casting technique.