Currency

In the present study, the effect of concluding and executing monetary treaties as well as membership of regional economic cooperation organizations (as a potential for using regional currency) on mutual exports between Iran and its 50 major trading partners during the period of 2000 to 2016 is investigated. The Generalized Commercial Gravity Model and Ordinary Least Square (OLS) regression of panel data with fixed effects are used. Countries' exports to each other are considered as dependent variables, and economic weight (log ratio of countries' GDP based on the purchasing power parity to each other), geographic distance between countries, real effective exchange rate and the degree of openness of the economy are independent explanatory variables. In addition, monetary treaties and the membership of the countries in the Asian Clearing Union, in the Economic Cooperation Organization (ECO) and in the Organization of Islamic Cooperation (OIC) are considered as dummy variables. The results show a positive and significant relationship between monetary treaties and the volume of exports of countries to each other. Also, the coefficients of dummy variables are significant and negative, indicating the lack of potential for the use of regional common currency among 51 countries over the considered period.

In this paper, using Evolutionary Game Theory, we study the evolution of currency as an important and influential problem in the world economy by presenting three new models. In examining the evolution of two different currencies, we prove that if the parties chose either of the two currencies with a fixed probability, it will not have an impact on the evolutionary process of these currencies. Considering a new action based on either the possibility of using any two currencies or a preference between the two currencies, we illustrate their impact on the evolution-ary process of currencies. In fact, we examine the impact of different actions and strategies on the evolutionary process between the two types of currencies.

This research aims to examine the impact of Big Data Quality (BDQ) components, including completeness, accuracy, format, and currency, on Big Data-driven Human Resources (BDHRP) management practices and Decision-Making Quality (DMQ) from the viewpoint of HR managers. It also seeks to identify the most impactful components among completeness, accuracy, format, and currency in the context of BDHRP and DMQ. A survey of HR professionals in 108 French organizations deploying Big Data Analytics systems revealed positive relationships between BDQ, BDHRP, and DMQ. Statistical analyses conducted with the Partial Least Squares Structural Equation Modeling (PLSEM) method showed a positive relationship between BDQ components and BDHRP, with currency and accuracy emerging as the most influential factors. Additionally, a strong positive relationship was found between BDQ components and DMQ, with currency and accuracy leading the way. The research also found a significant connection between BDHRP and DMQ, further underscoring the importance of effective HRM practices in enhancing decision-making quality. These findings contribute significantly to understanding the crucial role played by big data quality in BDHRP and decision-making, highlighting the potential for organizations to improve outcomes by focusing on currency and accuracy-related concerns. In practical terms, this research offers insights that can guide data quality practices, resource allocation, and strategic decision-making within organizations.