Knowledge Economy Studies

Abstract Nowadays, in organizations, selecting the appropriate knowledge management (KM) technology has become one of the senior managers’ concerns in the KM area. The purpose of this study is to provide a model to help decision-makers with identifying and selecting the most appropriate KM technologies in‌ an ‌Iranian bank. In this research, KM technologies and criteria to select them are identified and are classified by doing librarian studies. Two questionnaire surveys were conducted to weigh technology selection criteria and determining the value of technologies based on criteria among KM experts of bank. The weight of each criterion was assigned by applying the analytical hierarchy process (AHP) and finally, all the technologies were ranked by using the PROMETHEE II method. The results indicate that in top rank is networking technology followed by organizational group communication/private social network technologies, social networking technologies, web/multimedia presenting technologies, web conferencing technologies, large audience webinars technologies, communications and collaborations technologies, virtual three-dimensional immersive collaboration technologies, and finally groupware.

Abstract With the rapid advancement of technology and the expanding use of artificial intelligence (AI), extensive opportunities have emerged that are expected to fundamentally reshape orientations and approaches to educational practices in higher education, while simultaneously generating new challenges. Accordingly, examining the opportunities and challenges associated with the application of AI in higher education is of critical importance for developing appropriate strategies for its effective integration into educational processes. In this context, academics, as key agents in the implementation of teaching and learning activities, are well positioned to identify both the potentials and the limitations of this technology. Therefore, the present study aimed to explore academics’ attitudes toward the opportunities and challenges of using AI in education. This study adopted a qualitative research approach using a deductive qualitative content analysis strategy. Data were collected through semi-structured interviews. The study population consisted of faculty members and postgraduate students, who were selected using purposive and convenience sampling. Sample size was determined based on theoretical data saturation. The collected data were analyzed through systematic coding procedures. The findings revealed that, from the perspectives of faculty members and students, the opportunities and challenges of AI use in education could be categorized into three dimensions-affective, cognitive, and behavioral—based on the ABC attitude model; additionally, distinctions were identified across different educational contexts as reflected in the interview data. Overall, the results indicate that identifying academics’ attitudes toward the opportunities and challenges of AI in education constitutes a fundamental step toward the informed, responsible, and effective utilization

Abstract Ornamental houseplants and home greenhouses, as part of urban agriculture and indoor cultivation, are playing a growing role in environmental sustainability, household food security, indoor air quality, and urban well-being. This review asks how far the existing literature has actually moved beyond reactive disease detection toward a complete cycle of intelligent and sustainable plant protection — one that also covers early prediction, treatment decision support, and deployment in users' real homes. Following PRISMA 2020, we analyzed 55 primary studies published between 2015 and 2025. Thematic coding produced 23 codes across 9 categories and 4 dimensions: disease detection (64%), prediction and early warning (18%), treatment support (9%), and home/greenhouse deployment (7%). Reported accuracy reaches 95–99% in laboratory settings but falls to 70–85% once models are tested in real environments. We identified five structural gaps: a strong bias toward reactive over preventive approaches, a persistent laboratory-to-reality accuracy gap, an almost complete absence of data for indoor ornamental species, no use whatsoever of large language models for treatment guidance, and a weak theoretical connection to sustainable agriculture and Integrated Pest Management (IPM) principles. Future work should prioritize lightweight, generalizable models that integrate with environmental sensing under an IPM framework, thereby translating technical accuracy into measurable sustainability outcomes.

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 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 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 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 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 Without effective mechanisms for implementation, a code of ethics would not impact employees’ behavior. This study aims to inductively investigate implementing a code of ethics to improve the current understanding of this subject and make implementing a code of ethics in organizations more effective. To this end, the Grounded Theory (GT) approach is used. The research sample comprises managers and employees from 12 ICT companies in the Tehran Stock Exchange. Data were collected by conducting interviews and using the theoretical sampling method. On this basis, 23 HR managers/experts were interviewed. The collected data were analyzed using the approach proposed by Strauss and Corbin (1998). The findings indicate that organizations are driven to implement a code of ethics due to two main reasons: external pressure and internal needs. In implementing a code of ethics, they face challenges such as low top management support, improper financial situation, and unsupportive employee perceptions and attitudes. To implement a code of ethics, surveyed organizations take initiatives such as code of ethics definition and redefinition, communication, code of ethics training, punishing violations, and awarding obligations and social methods. Such initiatives can improve an organization’s ethical climate and create a distinguished identity, whereas they can yield undesired consequences if proven unsuccessful.

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.