درخت حوزه‌های تخصصی

People's work and personal life are not two separate categories, but a concept and should be considered together; because considering them separately causes us to have an incomplete understanding of both. Accordingly, the purpose of this study is to design a model for work-life balance of employees of the National Iranian Gas Company. The present research is applied in terms of orientation, descriptive in terms of purpose and survey in terms of strategy. In this study, using Delphi technique, the opinions of 15 experts on the factors affecting the balance of family work were identified based on the roadmap programs for reforming the administrative system of the country and 33 indicators. The sample size was 219 official employees and 61 contract employees of the official and contract employees of the National Iranian Gas Company (main company). Using confirmatory factor analysis, the mentioned dimensions were evaluated and tested. From the results of DEMETEL technique, it was observed that the factors related to monitoring, evaluation and development of organizational culture had the most relationship with other factors. The index of organizational culture development has the most impact and the factor of human capital has the least impact. The role and structure engineering index is the most influential in the next degree. Also, the criterion of factors related to management factors and technologies is very influential. The criterion of organizational culture development is also the least affected by other criteria. Engineering criteria role and structure have the highest impact and impact with other criteria studied. The criterion of human capital factors has the least impact with other variables. In this model, the criteria of engineering role and structure, development of organizational culture and monitoring and evaluation of the variable are causal and the rest are disabled.

The flow of information in the capital market is strategically important because it determines the path of investors' decisions. In this decision-making process, the managers of the companies can disclose timely and reliable information based on their cognitive and perceptual characteristics of capital market situations. This article aims to contribute to the capital market knowledge literature by presenting the framework of managers' inertia drivers in response to reliable disclosure of information. This study adopted mixed, both inductive and deductive approaches to develop an integrated framework, validate its practicability, and verify its effectiveness in selected firms listed on the Tehran Stock Exchange, respectively. In developing the framework and implementation procedure, the study employed a systematic screening data collection (qualitative) approach to review the managers' inertia drivers. Then, in this study's second phase, the Interpretive Rating Process (IRP) and Fuzzy Reference System are used to develop the framework of managers' inertia drivers in response to reliable disclosure of information. The study's results in the qualitative part indicate the determination of 8 driving areas of managers' inertia in the reliable disclosure of information. On the other hand, the quantitative section results showed that managers' overconfidence and excitability are the most influential fields in stimulating managers' inertia in the timely disclosure of information. Based on the results, it was determined that the excitability of managers' overconfidence in creating inertia causes managers' subjective estimates to cause exclusivity in information disclosure.

Today, choosing the suitable model for determining the portfolio of investment in financial assets is one of the critical issues of the attention of analysts and capital market activists, and investing in a portfolio consisting of mutual investment funds is the same. With this statement, the purpose of the article is to evaluate and compare the net assets value (return) of the Federation of Asian and European Stock Exchanges (FEAS) member countries by using support machine models in comparison with statistical models. The statistical and sample population included the data of 39 selected traded funds and FEAS members from 12 selected countries (including Iran) between 2014 and 2021. The data related to the mentioned funds were classified and analyzed using spss-modeler, rapid miner, and Weka software. They were tested with 24 support machine methods and 11 statistical methods, and the results showed that the prediction accuracy of statistical models is lower than that of support machine models. The Mann-Whitney test was used to determine the significance of this difference. Also, the results show that at the 95% confidence level, it can be claimed that the prediction accuracy of machine learning models is higher than statistical models. The average rating of machine learning models was (20.86) much higher than statistical models (10.85).