درخت حوزه‌های تخصصی

The development of the nation’s economy is supported by the growing diversity and enthusiasm of interest among academics, particularly in the transforming procedures with an entrepreneurial orientation. The study investigates changing entrepreneurial-oriented practices and the performance of SMEs utilizing a cross-sectional survey amongst the four top grocery registered SMEs operating in Ikorodu local government division of Lagos State, Nigeria. The tool for gathering data is a questionnaire with two sections: bio-data and research questions. While 100 respondents formed the sample size proportionally selected from the study focus. Pearson correlation is the statistical tool for data analysis. The study reveals that creativeness and alleviation of business loss have a highly significant impact on SMEs performance; with pro-activeness having a weak, but positive relevance on SMEs performance. In conclusion, to further improve and optimize entrepreneurial orientation practices and adapt to future threatening changes, firms need to strengthen the desire for healthy competition, identifying and targeting the weaknesses of their competitors. It is recommended that more innovativeness should be sought after as guiding principles for capability and adaptability to handle the complexity of the ever-changing business surrounding, while involvement in risk-taking should be adopted with inevitable zeal and willingness to attain organizational growth and improved performance. Pro-activeness should be given adequate consideration to maintain and surpass existing performance as opportunities come but once. Despite study limitations, future researchers may need to enlarge the sample size and create awareness of research conducted on SMEs generally. Keywords: Change, Entrepreneurial orientation, Performance, Practices, SMEs.

Enterprise risk management (ERM) represents a new paradigm that supports organizations in identifying, evaluating, and managing risks. Several factors encourage different organizations, especially banks, to design and apply ERM; among them, the possibility of financial problems and related costs, financial performance decline, growth opportunities, and independence of the board of directors can be pointed out. In addition, applying a suitable risk management strategy is a competitive advantage for supporting companies. Following the previous comprehensive studies, the present study was done by integrating a meta-composition approach and multivariate fuzzy network analysis. In this research, to identify effective factors on ERM based on empirical evidence and selection of studies, description and classification of selected articles, analysis of the content of selected research articles, and finally, the importance and refinement of the identified factors based on the Delphi expert opinion polling technique, and multivariate fuzzy network analysis was discussed. The study aimed to identify and rank the factors affecting the effectiveness of ERM of the firms accepted in selected banks in Iran. While reviewing studies, a semi-structured interview was conducted using an Exploratory-Descriptive Qualitative (EDQ) research design to determine the factors that affect ERM effectiveness. The interviewed experts were comprised of 20 university professors, CEOs, financial analysts of investment and brokerage companies, and senior auditors with accounting and management education. The literature review and the results of the interviews indicate five main themes that classify the factors affecting ERM effectiveness in firms. In this study, key factors were identified, and then the fuzzy Delphi technique was employed to rank and find the weight of the factors. The results showed that corporate governance, financial indicators, environmental indicators, company characteristics, and management indicators effectively enhance ERM's effectiveness. Accordingly, capital market analysts and investment companies should take a broader perspective and make decisions based on companies' financial risks instead of paying attention to companies' profitability and stock price changes. The results demonstrated that five factors determine ERM effectiveness: 1. corporate governance indicators (monitoring the board of directors and ownership structure), 2. Financial indicators (return on assets (ROA), earnings volatility, merger and acquisition (M&A) activities, financial deficiency, and capital opacity), 3. Environmental indicators (performance excellence, industry competition, audit firm credibility, environmental uncertainty, and industry), 4. Firm characteristics (financial leverage, firm size, and growth opportunities), and 5. Management indicators (management career and business diversity).

According to the form-out agreement the investor of the international petroleum agreement “farmor”, during the implementation of the project and due to various reasons such as lack of finance resources and/or incapability of contract performance, assigns provision of required investments for the purpose of performing an upstream oil project and/or certain operations (well drilling) under the upstream contract to other entities “farmee”, and generally in case of fulfillment of the aforementioned affairs by the farmee, the farmor transfers to the farmee a percentage of its participating interest in the international petroleum agreement upon permission from the host country. In line with this, through evaluating the upstream contracts in Iran, as well as the necessities for attracting investments for the purpose of performing such contracts, it is possible to find out the importance of clause concerning the assignment of contract in order for creating required legal instruments for entering into the farm-out agreement in accordance with the rules and regulations. So that through concluding farm-out agreement between the present and the potential investors, prevent emerging difficulties arising out of mere assignment of the contract to the potential investors during the implementation of upstream contracts.

The healthcare system holds a significance role in economic growth and development of any country. For assessing and evaluating the healthcare system, the relative prices of this sector are one of the crucial indicator. This study employs a stochastic dynamic general equilibrium model to examine the impacts of monetary, fiscal and technology shocks on the healthcare sector in the Iranian economy. The results indicate that positive monetary shock has direct impact on general inflation but tends to be reduced the relative prices of the health sector. Thus, the positive impact of this shock on the production of non-health goods is greater than the health sector. Also, positive shocks related to oil income, government health sector expenditures, and technology increase the production of health and non-health goods and have an inverse effect on inflation of this sector.

Detecting financial fraud is an important process in the activities of compa-nies. In the last decade, much attention has been paid to fraud detection techniques. Financial fraud is a problem with far-reaching implications for shareholders. Today, financial fraud in companies has become a big prob-lem. Companies and regulatory agencies must continuously develop their mechanisms to detect fraud. Machine learning and data mining techniques are currently commonly used to solve this problem. However, these tech-niques still need to be improved in terms of computational cost, memory cost, and dealing with big data that is becoming a feature of current financial transactions. In this research, machine learning techniques including logistic regression, neural network, and Bayesian linear regression were used to de-tect financial frauds in the Iranian stock market. According to the obtained results, the support vector machine model with radial kernel has the lowest RMSE and the highest accuracy criterion, and the support vector machine model with linear kernel and Bayesian linear regression has the highest RMSE and the lowest accuracy criterion for modeling the financial fraud of companies in they were Tehran stock market. Also, the models of artificial neural network model, Bayesian linear regression and support vector ma-chine model with linear kernel respectively had the lowest characteristic values and did not perform relatively well in detecting the existence of fi-nancial fraud in the companies present in the Tehran stock market.