Monetary & Financial Economics

Abstract The significance of fintech’s presence in the economy, particularly in the financial sector, is no longer a secret, and is underscored by the proliferation of recent studies examining the interplay between the performance of fintechs and macroeconomics on a global scale. According to international experiences, fintechs have significantly contributed to bolstering economic growth and controlling inflation by increasing access to financial services. Iran is host to over fifty operational fintech companies that have penetrated diverse facets of the business models employed by banks. Their presence may thus have an effect on the efficacy of the macro economy. A primary focus of economic policymakers in recent years has been the regulation of inflation. The fundamental inquiry is that how fintechs’ presence and emergence affect the inflation. Furthermore, what are the immediate and delayed consequences of fintechs’ existence on inflation? With the aid of time series data spanning 1991 to 2022 and the ARDL model, this paper examines the short- and long-term effects of fintechs on inflation. It categorizes fintechs into two distinct groups: those that engage in competition with banks and those that do not. By separating fintechs into competing and non-competing categories, the impact of each on inflation can be evaluated independently. The research findings indicate that the impact of different types of fintechs (competitor and non-competitor) on inflation differs, as does this impact during economic expansion and recession.

Abstract Objective: The purpose of this study is to investigate the effect of financial constraint on the speed of adjustment of business credit by considering the role of social responsibility of economic firms.
Methodology: This study employs an applied approach with a focus on causal and ex-post facto correlation. It examines the relationships among financial constraints, social responsibility, and trade credit adjustment speed. The statistical population comprises companies listed on the Tehran Stock Exchange, with data spanning the period from 2011 to 2023. Following a systematic elimination process for final sample selection, data from 146 companies were utilized for analysis. The hypotheses were tested using EViews 12 and Stata 14 software, employing the Generalized Method of Moments (GMM) approach.
Findings: Statistical results indicate that financial constraints have a direct and significant impact on trade credit adjustment speed. Specifically, when financial constraints increase, managers tend to rely more on credit financing, thereby accelerating the trade credit adjustment process. Furthermore, social responsibility influences this relationship; the interaction between financial constraints and social responsibility leads to an increase in trade credit adjustment speed.
Conclusion: Social responsibility is not merely an isolated ethical or strategic dimension; rather, it acts as a powerful moderating factor, influencing how financial constraints affect the speed of trade credit adjustment. The key finding is that companies with high levels of social responsibility are capable of overcoming, to some extent, the challenges posed by financial constraints, and can even maintain or enhance their trade credit adjustment speed.

Abstract Given the banking system's position in the country's economic system and banks' significant contribution to attracting financial resources, we expect the banking system's performance to significantly impact the economy. If the attraction of financial resources by banks leads to the allocation of resources to strengthen industry and production, it can contribute to economic prosperity. Therefore, the purpose of this study is to validate the policy-making model of transparency in the allocation of banking resources in Iranian knowledge-based companies. The research strategy is survey, and the research method is quantitative. The statistical population of the research was 380 managers, vice presidents, and all officials of knowledge-based companies located in the Mashhad Science and Technology Park, of which 190 were selected as a statistical sample using a stratified random method. The research tool includes a researcher-made questionnaire with 130 items. The partial least squares approach was used to analyze the data using PLS software. The research findings in the model in question include 6 categories in 15 components: economic, political, and legal independence; effectiveness in information quality and effectiveness in information access; dynamic monitoring; use of basic and applied systems; transparent allocation based on assets and investments; transparent allocation of resources based on alternative debt; prosperity of the knowledge-based economy; employment development; supportive policies; knowledge-based economy; challenges in implementing policymaking; and challenges of knowledge-based companies. The data results showed that the aforementioned model has the necessary validity. The findings of this study emphasize that identifying factors that enhance transparency in the allocation of Iranian banking resources can be useful in the process of improving the position of knowledge-based companies.

Abstract Commercial credit is the most important source of short-term financing for companies and commercial enterprises. Commercial credit is one of the methods by which companies provide financing for short-term periods. The purpose of this study is to investigate the impact of enterprise risk management on the supply and demand of commercial credit. Enterprise risk management has been used by Gourdon's comprehensive method (2009) and business credit by two methods of receiving and paying to measure it. The statistical population of the research is the companies admitted to the Tehran Stock Exchange in the period of 2013 to 2022, and by using the systematic elimination screening method, 136 companies have been considered as the final sample of the research. In the current research, Eviuz 12 software was used to test the research hypotheses. The results of the regression test showed that risk management directly affects the demand for commercial credit. But it does not affect the supply of commercial credit. Therefore, by controlling the risks facing the company, it is possible to obtain higher financing through commercial credit and reduce the need to keep cash in purchases.