Knowledge Economy Studies

Abstract Today, with the development of artificial intelligence, its application in different areas, including production and operations, has expanded. Explainable artificial intelligence (XAI) is a new research topic that has emerged with the development of artificial intelligence. This study aimed to investigate the applications of XAI in production and operations using the systematic review approach. For this purpose, a systematic review of the most recent studies published in the Science Direct, Scopus, and Emerald knowledge bases was conducted. After screening through different stages, 29 articles were reviewed and analyzed. The results showed that publications on XAI have been on an upward trend in recent years, with a significant increase observed from 2021 to 2024. Also, the fields of engineering, production, decision-making, and computer science are the major areas in which recent studies have been published. The results also suggested that the largest scope of XAI application was observed at the organizational level, followed by the industrial level. Based on the findings, the fields of production and operations, followed by logistics and supply chain, were the most frequently studied areas. Regarding the methods used, the SHAP method was the most commonly applied method in the XAI studies, followed by Integrated Gradient and SVM methods. In general, the results of this study showed that XAI is a new field of research that is gradually developing in terms of methodology and areas of application.

Abstract Financial services companies such as banks, brokerage firms, family offices, insurance companies, and trusts, provide advisory services to help clients achieve their investment goals. These services typically include offering investment solutions and discretionary portfolio management, where asset management is entrusted to financial experts. One of the main challenges in this field is recommending investment strategies that align with clients' needs and risk tolerance. In this study, a model was designed to assess investors' risk tolerance using advanced artificial intelligence (AI) and machine learning techniques. The model analyzed investors' demographic and financial data using regression algorithms to calculate their risk profiles. Then, an intelligent robo-advisor was designed to recommend the most suitable investment mix in S&P 500 companies' stocks based on individual investor profiles. The data for this study was extracted from the Federal Reserve's Survey of Consumer Finances (SCF), conducted between 2007 and 2009. The results of this research indicated that the use of AI and machine learning models can significantly improve the accuracy of assessing investors' risk tolerance. The proposed model, utilizing demographic and financial data from the SCF, successfully generated diverse risk profiles for investors. The designed robo-advisor intelligently analyzed these profiles and provided appropriate investment strategies for the S&P 500 index.

Abstract This systematic mapping study provides a comprehensive review of the existing literature on Fintech and its role in banking, exploring the current state, development, and future prospects of Fintech research. By analyzing 687 Fintech-related articles from academic databases covering the years 2015 to 2024, this article examines the evolution of Fintech. After describing the process of this phenomenon we identified a significant increase in research activity within this field during the past 5 years. This study offers a unique viewpoint, enabling both researchers and practitioners to reconsider the future direction and scope of Fintech research. This paper reviews the literature on Fintech and its interaction with banking, encompassing innovations in payment systems, credit markets, and insurance, with Blockchain-powered smart contracts also playing a role. It defines Fintech, presents relevant statistics and key insights, and reviews both theoretical and empirical studies. This review is centered around research questions, summarizing current knowledge, and concluding with recommendations for future research avenues.

Abstract This research study aims to investigate the role of Artificial Intelligence (AI) in efficient management of public financial systems and treasury functions. AI involves a broad array of knowledge, including various concepts, methodologies, strategic tools, and diverse applications. It can be defined as the study of systems that gather inputs from the environment and respond through actions.  Using AI in financial management and treasury presents distinct challenges and opportunities, as many treasury tasks have transitioned from physical to virtual processes, with automation advancing quickly. Financial and treasury teams are largely made up of knowledge workers who make decisions and perform analyses within dynamic frameworks. These frameworks must take into account both external and internal factors, as well as the effects of any actions on treasury outcomes. AI in finance and treasury functions closely mirrors the complexity of human nervous system, as it extends well beyond the basic automation. Like the nervous system, AI in these fields must process data rapidly and accurately, handling tasks such as data collection, classification, and integration into broader datasets. Today, neural networks within AI have advanced significantly and are widely applied across various treasury management areas, including early fraud detection, risk assessment, liquidity management, debt management, financial data quality control, extraction of hidden financial insights, accounting, and financial reporting. This review article aims to introduce readers to the various areas where AI can be applied in treasury operations, while also highlighting opportunities for enhancing accounting practices and driving digital transformation in treasury management. Additionally, it explores some potential research areas within these two fields.

Abstract Culture, infrastructure, organizational structure, and leadership are critical factors influencing the implementation of knowledge management processes in knowledge-based service organizations, thereby impacting the overall effectiveness of knowledge management. This research aims to introduce a comprehensive model that elucidates the interrelated factors affecting knowledge management processes in these organizations. Through an extensive review of existing literature, the authors developed a conceptual model that highlights these dynamics and their implications for practice. The model was rigorously tested and validated using a questionnaire distributed among various knowledge-based service companies in knowledge-based service companies, with 10 companies selected as the sample. A sample of 10 companies was selected for data collection, with data analyzed through Structural Equation Modeling using LISREL software. The research was conducted during a six-month period. The findings reveal that organizational culture, infrastructure, structure, and leadership enhance knowledge management capabilities significantly and influence knowledge management processes positively. Furthermore, these processes are shown to significantly improve the effectiveness of knowledge management, leading to enhanced communication, collaboration, and overall performance within service organizations, which ultimately fostered a more innovative and responsive organizational environment.

Abstract This study conducts a content analysis of Blockchain and cryptocurrency applications within the metaverse, focusing specifically on how these technologies shape users' financial behaviors. With Blockchain facilitating decentralized finance and secure asset ownership, and cryptocurrencies enabling fluid transactions in virtual environments, these technologies are integral to developing metaverse economies. By applying bibliometric and content analysis methods to articles from 2021 to 2024 in the Scopus database, the study identifies key themes and emerging trends in digital asset utilization, user engagement, and financial decision-making. The findings reveal that Blockchain and cryptocurrency applications foster new and varied financial behaviors among metaverse participants, shaping an ecosystem that is progressively diverging from traditional financial models.

Abstract Service quality (SQ) is crucial for customer retention, making it essential for managers to understand the factors influencing it. In today’s competitive landscape, organizations are increasingly investing in customer knowledge management (CKM) to enhance their service delivery. Although substantial research has been conducted on SQ, significant gaps persist, highlighting the need for further investigation. This study addresses these gaps by exploring the impact of CKM on SQ, with a particular focus on the mediating role of open innovation (OI). Adopting a quantitative approach, the research employs a descriptive correlational design and utilizes structural equation modeling for data analysis. The study sample comprises 200 companies in the information technology (IT) sector in Tehran, of which 139 completed the questionnaires. The obtained data were analyzed using AMOS and SPSS software. The findings indicate a positive and significant relationship between CKM and SQ, confirming that OI serves as a mediator in this relationship. Organizations that effectively integrate CKM with OI are more likely to achieve higher service quality, underscoring the importance of these strategies for enhancing customer satisfaction.

Abstract In the 21st century, the sales landscape has grown increasingly complex due to the shifts in behavioral, technological, and managerial practices. The performance of sales teams has been a long-standing topic of interest for both academics and marketing professionals. Understanding the factors that boost the performance of sales force is a key aspect of sales management and can greatly influence a company's success and survival. This study aims to explore the effect of digital marketing competencies on the sales performance of small and medium-sized enterprises (SMEs) in Ilam city. The research is applied in nature and utilizes a descriptive-correlational approach, with data gathered through surveys. The study's population consists of the sales forces of active SMEs in Ilam city. Given the small size of the population, a census sampling method was employed. After data collection, 132 valid questionnaires were used to be analyzed. The research instrument was a standardized questionnaire, with content validity confirmed by subject matter experts and reliability established through Cronbach’s alpha test. Data analysis was conducted using LISREL software. The findings indicated that digital marketing competencies have a significant and positive influence on the sales performance of SMEs in Ilam city. Moreover, technical-specialized, human-behavioral, and analytical competencies were also found to positively impact the performance of sales force. The results of this study suggest that digital marketing skills are critical for improving the performance of sales force. By providing sales teams with the necessary digital marketing tools and strategies, companies can enhance customer engagement and drive sales. Integrating digital marketing into sales operations can lead to better customer interaction, increased lead generation, and improved conversion rates. Sales professionals with digital marketing expertise are better equipped to navigate the evolving digital marketing landscape and meet the changing demands of modern consumers.

Abstract This paper examines the technological trends in Iran’s manufacturing sector over time, using the following science and technology indicators: human capital per capita from the Federal Reserve Bank of St. Louis, the ratio of research and development expenditures to gross domestic product calculated by the World Bank, the World Bank human capital index , the economic complexity index from Harvard University’s Atlas of Economic Complexity, the Global Innovation Index published by the World Intellectual Property Organization, and the manufacturing competitiveness performance as calculated by UNIDO. By analyzing the developments in various science and technology indicators within Iran's manufacturing sector, it can be concluded that these indicators showed an upward trend until the late 1380s (2000s in the Gregorian calendar). Overall, both the Iranian economy and the manufacturing sector were moving towards greater complexity and increased technology use. However, since the early 1390s (2010s in the Gregorian calendar) and the onset of economic sanctions, technological advancement in the manufacturing sector has stalled, leaving the Iranian economy in a relatively stagnant state, with some indicators even showing a backward movement. Despite this, given the potential of Iran’s economy, appropriate policymaking could partially reverse this trend. Considering the overall findings of this article, which indicate a decline in technological activities of the manufacturing sector over the past decade, we can argue that the lack of economic stability in the manufacturing sector, and macroeconomic developments in the country, have created an unfavorable environment for Iranian industries. This situation has led entrepreneurs and industrialists to focus more on maintaining existing performance rather than pursuing innovation and increasing competitive capacity, which has hindered efforts to expand activities and capture a larger share of the global market.

Abstract Digital transformation is essential for various businesses. Organizations seek to create a competitive advantage and improve productivity through digital transformation. The main objective of this research is to examine the impact of digital transformation on human resource productivity, considering the mediating role of strategic renewal. This research is applied in purpose and descriptive-correlational in nature. The statistical population of this study consists of 103 employees from the post office in Ilam, Iran.. Due to the small size of the statistical population and the risk of non-response bias, a census method was used for sampling. The data collection method was survey-based, as the data were primarily collected using questionnaires. The standardized questionnaire by Gavarila et al. (2022) was used to measure the digital transformation variable, the standardized questionnaire by Elyasi et al. (2018) was used to measure the productivity variable, and the standardized questionnaire by Fatehmi et al. (2017) was used to measure the strategic renewal variable.  The data was analyzed using LISREL software. The results indicated that digital transformation has a positive and significant impact on human resource productivity (0.89) and strategic renewal (0.72) of Ilam Post Office. Strategic renewal also has a positive and significant impact (0.84) on human resource productivity in Ilam Post Office.

Abstract Industry 4.0 technologies are rapidly transforming production processes and value creation in the global economy. In recent years, significant attention has been directed toward linking these technologies with sustainable development goals, particularly sustainable production. This study addresses the existing gaps in understanding the role of digital processes in achieving sustainable production, and presents a roadmap for leveraging Industry 4.0 technologies to support sustainable supply chains. Through a systematic literature review, we identified 15 sustainability functions that can benefit from Industry 4.0 technologies. This study outlines pathways for implementing these technologies to enhance the economic, social, and environmental dimensions of sustainable supply chains and highlights their importance in achieving sustainability goals.

Abstract One of the important challenges for organizations is that many strategic plans are not successfully implemented. Information technology (IT) tools can enable organizations to effectively implement their strategies by providing the necessary information infrastructure at various levels of the organization and among top and strategic managers. The main objective of this research is to identify the IT tools required to implement the strategies of Mellat Bank using the quality function deployment (QFD) approach. The research method in terms of outcome is categorized as developmental research, in terms of objective as applied research, and in terms of method as descriptive qualitative research. The statistical community in this study consists of experts and specialists from Mellat Bank Tehran. This research was conducted over one year, from 2019 to 2020. This research uses the method of extending the quality performance to translate strategies from high to low levels. In this study, the QFD method was used to translate strategies from high-level to low-level. To this end, QFD matrices were designed for each design subject, and the necessary IT tools for the expected functions were identified and scored using expert opinions. The findings show that for each strategy in an organization, we require specific information technology tools to execute the strategy within the organization properly. This study introduced the necessary tools for five strategic subjects, including integration and acceleration of design, production, and delivery of banking products and services, design and implementation of market penetration strategies, asset generation, improvement of credit processes, and financial and managerial independence of branches. Utilizing the identified IT tools will remove and reduce the barriers to implementing strategies and the strategic superiority of top-level and executive managers of organizations.