نشریه پژوهش های برنامه و توسعه
سال پنجم شماره 1 (پیاپی 17، بهار 1403)

By considering financial resource constraints, this article, stems from applied-developmental research, conducted to develop a suitable decision-making model for prioritizing the country's unfinished freeway projects. The main question of the research is: given the existing system documents and the specific internal and external conditions, what criteria and models can effectively guide decisions on prioritizing the country's incomplete freeway projects? The research employs an analytical-quantitative approach, with data gathered through both library research and field studies. Initially, relevant criteria for prioritization are identified by reviewing existing documents and previous studies, and these criteria, along with their sub-criteria, are compiled into a matrix. Expert interviews using the Delphi method are then conducted to assign weights to each criterion, resulting in a weighted criteria matrix. Finally, by applying network analysis (ANP) with Super Decision software and multi-criteria decision-making models (MCDM), a supermatrix of prioritized freeway projects is generated and prepared for implementation. This matrix enables the determination of project priorities using either single or multiple criteria. Additionally, the research identifies the potential to create a megamatrix that prioritizes all construction projects across the country.

Considering the stable and complex nature of the tourism system and its high impact on the trends, events and changes of the surrounding environment, it is necessary to identify the most important components affecting the sustainable development of rural tourism in Neyshabur city with a forward-looking approach. The population used in this study includes 60 experts and senior experts in rural tourism. In this study, 66 drivers were identified in the framework of PESTEL environmental analysis at three local, national and international levels. Of 66 drivers, 16 drivers were identified as driving forces in six dimensions. In this regard, the most important drivers affecting the sustainable development of rural tourism were identified as follow: of all the political factors, economic and political sanctions with an average effect of 4.68, of all  the economic factors, the high rate of inflation and long-term economic recession with 4.93, of all  the social factors, security in rural tourism destinations with an average effect of 4.70, of all  the technological factors, improving the quantity and quality of access to ICT and telecommunication facilities with an average effect of 4.57, of all  the environmental factors, adopting a policy Suitable environmental factors in rural tourism destinations with emphasis on sustainable tourism were identified with an average effect of 4.97 and among the legal factors, the special attention of the cultural heritage and tourism organization to the villages of the tourism destination/goal with an average effect of 4.73. Finally, it can be concluded that the success in achieving the all-round goals of the future of sustainable rural tourism in Neyshabur depends on the effective interaction between key factors and drivers. By using the identified factors and taking into account their priority level and their effectiveness on each other, it is possible to formulate, prioritize and implement solutions to maintain the favorable situation or to turn the unfavorable situation into favorable in different dimensions.

The growth of liquidity in recent years has increased sharply, with banks playing a significant role in this process through the creation of money via bank credits. This practice has led to imbalances in the banking network due to the location of new deposits. In this study, we intend to examine the process of money creation from nothing within the Iranian banking system. According to this theory, banks can create the entire amount of loans from nothing, based on the profitable opportunities available to them, and then seek to secure their reserves. To gain an insight regarding this process, the study garnered data from 18 banks listed on the Tehran Stock Exchange over the period from 2014 to 2022, using a panel data approach. The study employed two methods to explore the causal relationship between bank loans and deposit volumes. In the first method, two functions were estimated using the GLS estimation technique. The first function confirmed the causality from bank deposits to granted loans by estimating the relationship between deposit supply and loan provision. The second function, aimed at investigating money creation from nothing, established causality from loans to deposits. The second method involved performing a VAR test to determine the optimal number of lags between the two variables. Following this, the Granger causality test was applied, confirming the existence of a two-way relationship between loans and deposits.

Government budgeting is of great significance in assisting countries to achieve the economic and social objectives. With the expansion of modern technologies, especially artificial intelligence, new opportunities have emerged to improve financial and budgetary processes. With its advanced capabilities in data analysis and needs forecasting, AI can help increase accuracy and efficiency in resource allocation. These technologies not only transform existing processes but can also make a significant improvement in transparency and accountability in government financial affairs.

The objective of this study is twofold: first, to evaluate the efficiency of using artificial intelligence technologies such as neural networks and genetic algorithms in optimizing the allocation of government budgeting resources. Second, to determine the impact of these technologies on the transparency and accountability of budgeting processes to enhance public participation and trust.

The study employs a mixed-method approach. Data analysis is conducted through scenario modeling and and the Multipol software. Additionally, interviews and questionnaires with experts in related fields have been organized to provide our audience with a better understanding of the existing challenges and opportunities.

Findings indicate that the use of artificial intelligence can significantly increase accuracy, transparency, and efficiency in government budgeting. Artificial intelligence enables policymakers to adjust resource allocation based on more accurate data and optimized forecasts.

The issue of foreign investment and its importance for economic growth and development has long been a concern for policymakers in Iran. To address this, various governments have enacted new laws, invested substantial physical and human capital into the public sector, and set ambitious goals and visions. However, these efforts often overlook the capacity of the executive environment, leading to minimal achievements. Instead, these initiatives have overburdened the executive system, draining its resources.This study, using a new approach called Problem-Driven Iterative Adaptation (PDIA), highlights several key issues undermining the government's ability to attract foreign investment. These include the government's inappropriate paradigm regarding foreign investment, an overemphasis on centralized solutions, a reliance on practices from other countries without adapting them to local conditions, bureaucrats' lack of a shared understanding of the government's capacity, insufficient attention to the local context and the interactions of key actors, and reformers' disregard for existing authority structures.The findings suggest that the government need to espouse a problem-oriented approach rather than a solution-oriented one. It is crucial to consider the local context and the key players involved when formulating policies. This article underscores the need for policymakers to tailor their strategies to the specific challenges they face and to align policy agendas with the actual capacity of the executive environment.

Given the potential impact of blockchain technology on the banking industry, it is crucial to explore how this technology influences business models within the sector. This understanding can provide valuable insights into how value is created, delivered, and captured in banking. The main objective of this article is to present a blockchain technology framework as applied to fintech in the financial and banking sectors. This study employs metacomposition to identify key factors, the fuzzy Delphi method to evaluate, refine, and localize the model, and the Dimetal technique to analyze relationships and effects. The research involved a systematic review of 849 articles (ranging from 1395 to 1402 in the Iranian calendar for domestic articles, and from 2016 to 2023 for international articles), from which 73 articles were selected and analyzed using content analysis. Subsequently, 7 dimensions, 23 components, and 102 indicators were validated and localized with the assistance of 17 experts. In the final stage, these dimensions and components were prioritized with expert input, leading to the extraction of the final framework. The results indicate that fintech-driven blockchain technology enhances value creation in the dimensions of innovation and technology, transparency, security, quality, speed, business models, and collaboration, providing strategic capabilities to financial and banking businesses.