Knowledge Economy Studies

Abstract Cryptocurrencies, as one of the emerging asset classes, have gained significant popularity in recent years. Accurate forecasting of cryptocurrencies’ prices has become highly attractive for both researchers and investors due to their volatile and non-linear price behavior. However, predicting the cryptocurrencies’ prices accurately remains challenging due to their substantial fluctuations and complex dynamics. Research findings indicated that the methods of deep learning and neural networks outperform traditional econometric approaches in forecasting financial and economic time series. Among the techniques of neural network and deep learning, various types of Recurrent Neural Network (RNN) models have been proven to be effective. This study employed three Recurrent Neural Network architectures—RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—to predict the logarithm of the prices of four major cryptocurrencies of Bitcoin (BTC), Dogecoin (DOGE), Ethereum (ETH), and Ripple (XRP). Daily time-series data from January 17, 2018, to December 18, 2024, were utilized for this purpose. The data were collected using the cryptocmd python package. The experimental results which were assessed using four metrics—Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE)—revealed two key findings: First, as the forecasting horizon increases, the required input size for achieving the best predictions increases for all models. Second, the LSTM model demonstrates a superior performance in predicting the prices of major cryptocurrencies for 1-day and 30-day horizons, whereas the GRU model exhibits the lowest prediction error for a 7-day horizon. These findings provided valuable insights for estimating the mean equation, which is instrumental in forecasting the expected returns of cryptocurrency assets for risk management purposes.

Abstract Generative Artificial Intelligence (AI) models, such as large language models, are transforming various sectors of the knowledge economy, including education, research, development, and data analysis, due to their ability to generate new contents. However, hallucination, which refers to the generation of content that appears plausible but lacks scientific basis, presents a significant challenge to the safe adoption of this technology in critical applications such as financial analysis and market prediction. This study aims to explore the effects of hallucinations on productivity of the knowledge economy and proposes some approaches to mitigate them. Through conducting a systematic literature review and qualitative analysis, different types of hallucinations in generative AI models are identified, and their effects on trust and productivity in knowledge-based systems are examined. The review of hallucination reduction methods indicates that the approaches utilizing reinforcement learning with human feedback enhance the reliability of the generated content by correcting errors in the model’s output through repeated adjustments. Finally, this study presents a hybrid approach to hallucination reduction in knowledge economy. This approach is based on three techniques of prompt engineering, retrieval-based generation, and self-improvement through feedback and reasoning. The results provide a foundation for future research on managing AI risks and enhancing the productivity of the knowledge economy.

Abstract Regulations regarding financial technologies (fintech) refer to laws that aim to balance financial innovation with the maintenance of security, transparency, and financial stability in digital markets. This research study aims to analyze the trends, challenges, and future directions in the field of fintech regulations using a Bibliometric approach. To this end, relevant keywords were initially searched in Scopus and Web of Science databases, resulting in the selection of 191 papers as the research dataset. Subsequently, the Bibliometrix package in R was employed to identify the influential authors and institutions, analyze temporal trends, and detect the related research clusters. The results indicated that research on fintech regulations has shown a significant growth. Core challenges in this field include maintaining data security, achieving international regulatory coordination, and facilitating the acceptance of financial innovations. Ultimately, this study provided some insights into the challenges of regulating innovation and offered guidance for researchers, policymakers, and fintech professionals in understanding the current trends and designing more effective regulatory frameworks to support the financial innovation.

Abstract Digital transformation is probably the most attention-grabbing phenomenon in contemporary business-related and managerial discourses. Nowadays, organizations have acknowledged the fruitful impacts of digital transformation on their businesses, so they are trying to implement this transformative journey. However, they have recognized that this is not a short-term project or an easy-going process, so they are struggling to find an appropriate organizational asset to empower them in following this journey appropriately. In this theoretical study, we have introduced social media analytics as such an asset. Accordingly, drawing on the dynamic capability view, we attempted at developing eight propositions and elaborating a theoretical framework in which social media analytics is seen as a great source of data-driven dynamic capabilities (i.e., data-driven environmental scanning, data-driven organizational agility, and data-driven product / service innovation).  These propositions are, in turn, positively associated with the main building blocks of digital transformation implementation including digital disruption detection, digital strategy formulation, and digital value creation. Our theoretical framework describes some of the mechanisms through which social media analytics can benefit organizations in steering their digital transformation journey.

Abstract In the contemporary economic landscape, the implementation of monetary policies constitutes one of the primary responsibilities of central banks, aimed at objectives such as money creation, liquidity management, inflation containment, and recession prevention. With the emergence of advanced technologies, Artificial Intelligence (AI) has gradually evolved into a powerful instrument for enhancing the efficiency and effectiveness of these policies. The adoption of intelligent data processing and analytical techniques enables central banks to monitor liquidity flows, calibrate money supply levels, and forecast economic behaviors more accurately. This study, employing a descriptive-analytical methodology and grounded in legal and economic sources, seeks to address a central question: To what extent can AI contribute to improving the processes of money creation and liquidity control within central banks, and what challenges and considerations are involved in its implementation? The findings suggested that strategic integration of AI into monetary policymaking—particularly in the domain of money creation—can lead to more informed decision-making, mitigation of financial risks, and increased policy effectiveness, thereby fostering greater public confidence in central banking institutions. Nevertheless, such integration necessitates adherence to specific prerequisites and regulatory frameworks, as the application of AI in the field of economics still requires consistent human oversight due to the limited specialized expertise at the intersection of economics and technology.

Abstract One of the dire concequences of innovation is rapid reaction to developments and changes in the environment, and this depends on the managers’ ability to reflect on the changes in the environment and various aspects of business. The aim of this study is to investigate the effect of threat-oriented interpretation in climate changes on innovation and its dimensions (behavioral innovation, product innovation, process innovation, market innovation, and strategic innovation). Statistical population of this research consists of executives who belong to chemical and petroleum industries listed on the stock exchange. The data was collected using questionnaires from 79 companies. Structured Equation Modeling (SEM) and Partial Least Squares (PLS) methods were employedto analyze the data. The results showed that the threat-oriented interpretation in climate change has no direct effect on innovation. However, it indirectly and negatively affects innovation. The innovation capacity generally mediates this relationship.

Abstract In a world where buying and selling digital currencies has become an integral part of the global economy, digital currency exchanges play a very important role. Therefore, the purpose of this article is to identify the effective factors in choosing digital currency exchanges. The present study is a qualitative research in terms of method, exploratory in terms of approach, applied in terms of purpose, interpretive in terms of data collection or the nature and method of the research. The statistical population of the research is the digital currency traders (inside Iran). Purposive sampling was used to select the participants, and 16 people were interviewed following the rule of theoretical saturation. The data collection tool is interview. Based on the findings of the research using the theme analysis method, six effective factors in choosing digital currency exchanges included security, liquidity, and volume of transactions, authentication, fees, support, and diversity in the number of cryptocurrencies. Finally, three categories of low risk, good service, and financial advantage were introduced.

Abstract The implementation of Internet of Things (IoT) technology in food supply chains faces significant barriers that hinder its potential to enhance traceability, efficiency, and sustainability. This study identifies and analyzes these barriers by conducting a case study of Kalleh Company, a leading Iranian food producer, using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. Through conducting a literature review and expert interviews, 11 key barriers were categorized into technological, financial, human capital, regulatory, and infrastructural dimensions. The DEMATEL analysis revealed critical insights into the causal relationships among these barriers. Inadequate infrastructure emerged as the most prominent barrier, heavily influenced by financial constraints such as high implementation costs and limited funding access. Meanwhile, lack of interoperability was identified as the strongest causal barrier, creating systemic challenges by preventing seamless integration across IoT systems. Interestingly, while the shortage of skilled labor showed balanced influence/dependence, its high prominence underscored its operational significance. The study highlights the complex interdependencies among IoT adoption barriers, demonstrating that infrastructure limitations cannot be resolved without addressing the underlying financial and technological challenges. These findings suggest that successful implementation of IoT requires coordinated strategies addressing multiple dimensions simultaneously including standardization efforts, financial support mechanisms, and workforce development programs.

Abstract The Fourth Industrial Revolution (Industry 4.0) has provided significant opportunities to improve performance and productivity in maritime industries through emerging technologies. However, the multiplicity and diversity of these technologies have turned their selection and prioritization into a fundamental challenge for decision-makers. This research study aims to identify and prioritize key Industry 4.0 technologies in maritime industries. Through conducting a systematic literature review and interviews with 15 industry experts, 59 applications within 12 key technologies were identified. Then, using the Best-Worst Method (BWM) of multi-criteria decision-making, the relative importance of technologies and their applications were determined. The results showed that Digital Twin Systems with a weight of 20.02 is the most important technology, followed by Robotics and Automation with a weight of 16.11, and Artificial Intelligence (AI) and Machine Learning with a weight of 12.08. Among the applications, operation optimization through scenario testing, autonomous vehicle guidance in port areas, and staff training with virtual models obtained the highest priorities. By providing a systematic framework for technology prioritization, this research can assist decision-makers in maritime industry to allocate optimal resources and reduce investment risks.

Abstract Financial distress analysis is one of the essential and important topics in financial management, playing a vital role for investors, creditors, and other users of financial information. Predicting the likelihood of financial distress in companies before it occurs can provide valuable opportunities for managers in terms of risk management, as well as for investors and creditors in making better decisions. In this research, using data from manufacturing companies listed in Tehran Stock Exchange from 2011 to 2018 that have faced financial distress, the factors influencing financial distress were identified and, its prediction was examined using advanced Artificial Intelligence (AI) algorithms, including system dynamics and fuzzy logic. The results indicated that key variables such as production and demand played a decisive role in the occurrence of financial distress. These findings not only provided a tool for more accurate predictions of financial distress but also offered a framework for preventive policymaking in companies.

Abstract A knowledge-based economy leads to a more efficient and dynamic national economy, contributing to the creation of more products, services, and innovation. Consequently, it brings about the outcomes of a stable economy, characterized by consistent and sustainable tax revenues. By focusing on innovation, advanced technologies, and specialized human capital, a knowledge-based economy plays a crucial role in improving the economic structure of the country. A knowledge-based economy is one in which the production, distribution, and application of knowledge are the main drivers of economic growth, wealth creation, wealth distribution, and job creation in all industrial sectors. The purpose of this research study is to investigate the impact of the knowledge-based economy on tax revenues in Iranian provinces. From the perspective of its purpose, this study is an applied study, and in terms of methodology, it is a panel data study, which has been conducted econometrically using the FMOLS panel data method. The findings indicated a positive and significant relationship between the components of the knowledge-based economy and tax revenues. As the components of the knowledge-based economy increase in Iranian provinces, tax revenues also rise. Furthermore, the model estimation, with the inclusion of auxiliary variables, showed that inflation rate, Gross Domestic Product (GDP), and government expenditures have a positive and significant impact on tax revenues, while the unemployment rate affects tax revenues negatively and significantly. Therefore, regional policymakers should prioritize enhancing human skills, expanding knowledge-based institutions, promoting innovative and creative centers, and developing technological infrastructure to increase tax revenues.

Abstract Strategic knowledge management is recognized as a fundamental pillar of organizational success in today’s knowledge-driven era. This approach surpasses traditional information management by positioning knowledge as a strategic asset central to organizational decision-making. The primary objective of this study is to examine the effect of strategic knowledge management on business performance in small and medium-sized enterprises (SMEs) located in Ilam City. In terms of purpose, the research is applied, and methodologically, it adopts a descriptive-survey design. The statistical population of the study comprises 320 managers and experts from active SMEs in Ilam City. Based on Morgan’s table and using a simple random sampling technique, a sample of 175 participants was selected. Standardized questionnaires were employed for data collection. The strategic knowledge management variable was measured using the instrument developed by Shaik et al. (2024), while business performance was assessed using the questionnaire developed by Depino-Besada et al. (2025). Content validity of the instruments was confirmed through expert evaluation, and reliability was tested using Cronbach’s alpha coefficient. Data analysis was conducted using SPSS and LISREL software. The findings revealed that strategic knowledge management has a positive and statistically significant impact on the business performance of SMEs in Ilam. Moreover, it was found to have a significant positive influence on exports, innovation, and the expected growth of these enterprises.