درخت حوزه‌های تخصصی

Board diversity is a topical discourse in firm governance and management. Diversity came up as a way of eliminating discrimination in employment and making sure there is equality, inclusion and affirmative action in the way firms do business. Board diversity has produced mixed results in relation to firm performance. On the one hand, diversity enables good governance to take place, ensures satisfaction of stakeholders and the firm to attain competitive advantage. Contrary, diversity may come with difficulties in communication, boardroom fights and decreased productivity among a plethora of negative contributions. The study investigates the various factors that affect board diversity from a Zimbabwean context. The Zimbabwe Stock Exchange’s 35 firms’ data is analysed to estimate the relationship between board diversity and firm performance. The study employed the quantitative methodology to establish factors that influence board diversity on firm performance of thirty-five (35) firms listed on the Zimbabwe Stock Exchange using panel data collected over the period 2009 – 2015.The major factors that promote diversity are firm size, liquidity, leverage, operating experience (years listed), market share (Tobin’s Q) and being in the service sector. On the other hand, board size, being in the food, financial, real and industrial and manufacturing sectors negatively and significantly influence diversity. Based on the above results, the study recommends that companies should come up with diversity-enabling policies to enhance firm performance.

Objective: Due to globalization, its economic impacts, and the capacity of family businesses to create socio-emotional value on an international scale, this study aims to establish a comprehensive theoretical framework for the internationalization of family businesses.Methods: Using a mixed-methods approach, 1,727 articles from the Web of Science database were identified over a decade, and 42 relevant theories were systematically reviewed. A subsequent narrative review further examines these theories in the context of family business internationalization.Results: These theories are categorized through two approaches: first, a multidisciplinary approach, which includes general management theories, internationalization theories, and family business-specific frameworks; and second, an interdisciplinary approach, organizing these theories within the framework of theoretical evolution. This evolutionary process starts with theory building, continues with theory development, and concludes with theory testing.Conclusions: The primary contribution of this study is the identification of core assumptions within each theory related to family business internationalization. Additionally, by extracting key concepts based on content correlation, this research presents a conceptual model that enhances understanding of the theoretical framework and highlights gaps in the evolution of these theories. These findings provide a valuable foundation for future research in the dynamic and evolving field of family business internationalization.

In the present study, the role of quantitative easing on the stability of financial markets in Iran has been analyzed from the quarterly data of 2006-2021 using the econometric model with vector error correction model (VECM). To estimate the model from some observable variables, the effects of quantitative easing on financial market variables, including (liquidity volume, capital adequacy rate, credits granted to the private sector by banks, bank deposit interest rate, stock market index and exchange rate) were estimated. The data was extracted from the official website of the Securities and Exchange Organization and the Central Bank. In VECM models, the estimation was done in both short-term and long-term periods with the aid of Eviews software. The findings of the research, based on the estimation of long-term and short-term relationships, show that quantitative easing has a positive and significant effect on the stability of financial markets. With the MP1 index increasing by one percent, the Financial Market Stability Index increased by 4.157 percent.

The present study aims to investigate the mediating role of reflective identity in the relationship between neuroeconomics and entrepreneurial decision-making . The research method is applied in terms of purpose and descriptive survey based on structural equation modeling in terms of nature and procedure. A questionnaire was used to collect field data. The face and content validity of the questionnaire was confirmed by experts and the construct validity was calculated using confirmatory factor analysis. The reliability of the research tools was calculated using Cranach’s alpha test and a composite reliability higher than 0.7 was achieved, which reveals the appropriate validity of the research tools. The statistical population of the research consists of 110 entrepreneurs based in the Science and Technology Park of Zahedan. Morgan's table was applied to determine the sample size, and 86 individuals were selected as the statistical sample by using simple randomization. For analyzing the research data and answering the research hypotheses, SPSS23 and Smart PLS software and the structural equation modeling test were used. The research findings indicated that reflective identity has a mediating role in the relationship between neuroeconomics and entrepreneurial decision-making of the entrepreneurs based in the Zahedan Science and Technology Park. It should be mentioned that a better understanding of the issues pertaining to the upcoming choices for entrepreneurs will lead to better decisions. Therefore, this understanding of the neural basis of decision-making can lead to entrepreneurial decision-making in a special way, which often includes risky choices under conditions of uncertainty.

The accurate imputation of missing values in time series data is paramount for maintaining the integrity and reliability of analyses and predictions. This article investigates the effica-cy of various missing values imputation methods, encom-passing well-known machine learning and statistical tech-niques. Moreover, for a better understanding, they imple-mented two financial data time series: S&P 500 and Bitcoin markets spanning from 2016 to 2023 on a daily frequency. Initially utilizing complete datasets, controlled missingness was introduced by randomly removing 45 data points. Then, these methods applied multiple imputation strategies for estimating and substituting these missing values. Experi-mental evaluation yielded insightful findings regarding the performance of the different methods. The examined ma-chine learning methods, including k-Nearest Neighbors (k-NN), Random Forest, Deep Learning, and Decision Trees, consistently outperformed their statistical counterparts, such as Mean Imputation, Regression Imputation, Hot-Deck Im-putation, and Expectation-Maximization Imputation. Nota-bly, Random Forest emerged as the most effective method, showcasing superior performance in terms of accuracy and robustness. Conversely, the Mean Imputation method exhibited com-paratively inferior outcomes, suggesting its limited suitabil-ity for financial time series data. This research contributes to the ongoing discourse on data integrity within finance ana-lytics and serves as a comprehensive guide for practitioners seeking optimal missing values imputation methods. The empirical evidence provided herein advances the under-standing of imputation techniques' relative performance and their application in financial data, facilitating enhanced de-cision-making processes and yielding more reliable predic-tions.

Energy, besides other factors, production is considered the main factor in growth and economic development, and economics can play beneficial roles in the performance of different sectors. Hence, the country authorities should try to predict anything more precisely regarding energy consumption in the proper planning and guidance of consumption to control the way they desire energy demand and supply parameters. This paper aims to evaluate the hybrid model of artificial neural networks and genetic algorithms in forecasting demand energy to predict energy consumption in the country. A case study is energy consumption in Iran's transportation sector. So, for this review, we use the annual data on energy consumption of transport as a variable output of forecast models and data from the entire country's annual population, GDP, and the number of vehicles as the input variables. Evaluation results showed that themodel ofArtificial hybrid model of Neural Networks (ANN) and Genetic Algorithm (GA), campared toother models, has the highest accuracy in predicting energy demand in the transportation sector