Knowledge Economy Studies

Abstract Outsourcing is a recommended strategy for enhancing competitive advantages. Many foreseeable issues within organizations can be mitigated by eliminating inefficient internal activities from the Knowledge Management (KM) process. One effective approach is to outsource parts of the KM process to an external third party. A key aspect of outsourcing is the selection of appropriate service providers. This study identifies and evaluates the criteria and dimensions for selecting KM outsourcing providers within an insurance company by reviewing the prior research and validating the findings with both industry and academic experts. A framework consisting of nine factors and 35 indicators was developed and validated using the Fuzzy Delphi method. Subsequently, the Fuzzy Analytic Network Process (FANP) and the DEMATEL technique were employed to analyze the data and provide a comprehensive decision-making framework for selecting the most suitable KM outsourcing providers. The findings indicated that “quality” is the most influential criterion in the system, while it is the least influenced by other factors. Conversely, “specialized organizational features” are significantly affected by other criteria. According to FANP results, “experience” holds the highest importance with a weight of 0.30773, whereas “organizational culture” ranks lowest with a weight of 0.02716. Finally, the outcomes of the Fuzzy Delphi and DEMATEL methods were applied in a real-world case within an insurance company to select the optimal KM service provider from two candidate firms.

Abstract This paper presents a comprehensive analysis of the challenges, opportunities, strategies, and the role of stakeholders in international tourism branding within the context of the knowledge economy. Moving beyond outdated, advertisement-based paradigms, modern tourism branding requires dynamic knowledge and effective management to achieve global competitiveness. This study utilizes thematic analysis of qualitative-exploratory research and is informed by interviews conducted with 14 prominent experts in the fields of branding and marketing. The reliability of the coding process was confirmed through a test-retest method, yielding a coefficient of 89.33%. The findings revealed substantial potential in areas such as digital technologies, indigenous knowledge production, community engagement, knowledge-based brand cluster formation, and the integration of emerging technologies (e.g., AR/VR and data analytics) to strengthen competitive advantages. However, several barriers remain. These include insufficient data and technology infrastructure, difficulties in converting knowledge assets into brand value, weak brand governance, and resistance to innovation among conservative stakeholders. Strategically, the paper supports knowledge-based brand governance models built on a tripartite collaboration between government, academia, and industry. Most importantly, this study emphasizes the critical role of digital training for local labor forces and the implementation of knowledge-based metrics for brand evaluation. It redefines the role of stakeholders: tourists are reframed as knowledge prosumers, universities as brand documenters, and governments as facilitators of smart governance. Ultimately, this paper proposes an innovative framework that views the tourism brand not as a promotional tool, but as a dynamic, evolving structure embedded in knowledge ecosystems.

Abstract Quantum Bayesian Machine Learning (QBML) is an emerging field at the intersection of quantum computing, machine learning, and financial sciences. It has enabled the development of more accurate predictive models, optimal risk management, and intelligent portfolio optimization. With the rapid growth of data and increasing complexity of financial markets, classical computational models are no longer sufficient to meet the demands of modern technological needs. Consequently, combining the power of quantum computing with machine learning algorithms has created opportunities to develop models with enhanced accuracy and efficiency. QBML has garnered attention from researchers due to its ability to manage uncertainty precisely and provide probabilistic inferences, particularly in market prediction, risk management, and portfolio optimization. Despite significant theoretical advancements, challenges such as quantum hardware limitations, algorithmic complexity, poor data quality, and the gap between theory and practical applications have hindered widespread adoption of these technologies. Systematic and Bibliometric analyses indicated that while the field is rapidly growing, there remain serious gaps in practical implementation and algorithm performance evaluation. The findings of this study emphasized that fully exploiting the potential of QBML in financial systems requires developing hardware and algorithms, conducting empirical research, and fostering interdisciplinary collaborations. Moreover, the scientific mapping conducted in this study provided a useful framework to guide future research and develop practical applications that can transform analytical and decision-making methods in finance.

Abstract In current business landscape, value Co-Creation (VCC) has emerged as a key principle in service marketing and management. As organizations increasingly aim to involve customers in meaningful ways, firms are engaging them more actively in designing new products and services. When offerings are developed through a co-creation process that reflects the customers’ needs and preferences, they are more likely to succeed in market over the long term. Therefore, understanding the factors that drive VCC is essential for organizational success. While previous research has explored various determinants, relatively few studies have investigated how customer personality and website personality jointly influence VCC, particularly considering the mediating role of the brand trust. This study addressed this gap by combining insights from personality traits and digital interface characteristics, thereby contributing to the literature on technology-mediated co-creation. A descriptive-analytical and correlational approach was used in this paper. The statistical population included online retail customers who had made at least two purchases on tourism websites. The participants were selected using a non-probability convenience sampling method from customers of Eli Gasht, Eghamat24, and Koja Ro. A structured questionnaire was designed and administered, producing 253 valid responses that were analyzed using appropriate statistical methods. The instrument’s validity and reliability were confirmed through expert review and Cronbach’s alpha, respectively. Findings revealed that both customer personality and website personality have a meaningful impact on VCC, with customer personality exerting a stronger influence. Additionally, the brand trust served as a crucial mediator in the relationship between personality factors and co-creation. These results underscored the importance of aligning digital platforms with the traits of their users and highlighted the strategic role of brand trust in promoting collaborative value creation.

Abstract This study examined the barriers to adopting Artificial Intelligence (AI) in desalination supply chain (SC), a sector increasingly seen as vital for tackling global water scarcity. Despite AI’s proven ability to improve efficiency, sustainability, and decision-making in complex supply chains, its implementation in desalination systems encounters formidable challenges. Through a comprehensive literature review and expert consultations, sixteen barriers were identified and analyzed structurally using the MICMAC approach. The results showed that four factors are the most influential barriers and serve as bottlenecks for successful AI adoption: lack of funding and capital, lack of standardization and interoperability, shortage of specific skills and talent, and data privacy and security concerns. The present study emphasizes the need for integrated strategies that include financial support, common standards, skill development programs, and strong data protection frameworks. It also highlights the importance of collaboration among governments, private sector stakeholders, and research institutions to overcome systemic obstacles. The findings may not only offer insights into the key drivers of AI implementation in desalination but also provide a roadmap for policymakers and industry leaders aiming to develop more resilient and sustainable water management systems.

Abstract With the expansion of modern technologies and the pervasive influence of digital space in our daily life, financial delinquency among children and adolescents has become a significant social and economic challenge. This phenomenon, occurring in both physical and digital environments, has widespread consequences for  individuals, their families, and society.  This study used a comparative approach, utilizing data from cases recorded in Tehran Juvenile Court, to examine the underlying factors and consequences of financial delinquency and the economics of crime in these two contexts. The findings revealed that multiple factors—including family economic status, parental supervision weaknesses, behavioral disorders, cultural changes, and broad access to digital tools—can contribute to the occurrence of these offenses. In physical environments, the economic and social impacts primarily affect the family and the adolescent’s immediate social surroundings, whereas in the digital space, due to the scale, speed, and anonymity of offenders, the consequences are more complex and, in some cases, cross-border. The results highlighted the necessity for preventive policies, promotion of economic and digital literacy, strong support systems, and improvement of judicial frameworks related to children and adolescents. These insights can assist policymakers and researchers in reducing the financial and social harms associated with juvenile delinquency in the digital era.

Abstract This study investigated the role of Artificial Intelligence (AI) in enhancing strategic decision-making within volatile economic environments, focusing on knowledge economy. Employing a mixed-methods approach, the research integrated a Systematic Literature Review (SLR) with secondary data analysis from 300 organizations across finance, manufacturing, retail, and healthcare sectors. Qualitative insights from the SLR identified three core themes including AI-enhanced organizational agility (75% of studies), ethical and implementation challenges (65% of studies), and knowledge economy integration (70% of studies). Quantitative findings confirmed these themes, revealing that organizations with advanced AI adoption achieve an average 25% improvement in decision accuracy and a 30% increase in operational resilience. However, ethical concerns such as algorithmic bias and privacy issues led to a 12-18% reduction in perceived trustworthiness, reported by 20% of analyzed organizations. The study extended dynamic capabilities and resource-based view theories by proposing a unified framework that integrates agility, ethical governance, and knowledge-driven alignment. Practically, it offered managerial guidance on AI deployment and ethical protocols, while advising policymakers on regulations to ensure equitable AI access, particularly for small and medium enterprises (SMEs). The research positioned AI as a critical strategic asset for navigating volatility while emphasizing risk mitigation.

Abstract Paper industry is one of the key sectors in Iran. Similar to other industries, it requires the adoption of electronic facilities—particularly e-commerce—in order to achieve growth and progress. The present study aimed at identifying the barriers to implementing e-commerce in Iran’s paper industry. From a methodological standpoint, this research study is exploratory in nature and employs a qualitative approach. Initially, the theoretical foundations of barriers to implementing e-commerce in Iran’s paper industry were examined. Subsequently, semi-structured interviews were conducted with 10 experts and professionals engaged in paper-related industries and electronic commerce in Tehran, each with over five years of relevant experience. Following a content validity assessment by experts, a questionnaire was developed using the Delphi technique. After conducting three iterative rounds and achieving expert consensus, the barriers were classified into five categories of regulatory and institutional barriers, financial and infrastructural barriers, organizational and managerial barriers, cultural and behavioral barriers, and technological and operational barriers. Data analysis revealed a total of 46 barriers to e-commerce implementation in Iran’s paper industry. Theoretically, this study enriches the literature on e-commerce adoption barriers in developing economies by providing a sector-specific model, while practically it offers policymakers and industry managers actionable strategies to overcome the structural, behavioral, and contextual challenges in Iran’s paper industry.

Abstract Despite the growing importance of text mining in knowledge management, a comprehensive analysis of its evolution, key contributors, and emerging trends has remained limited. This study addresses this gap by conducting a bibliometric analysis of the field from 2003 to 2023, focusing on long-term trends, influential actors, and thematic shifts. To this end, a scientometric analysis was conducted using data sourced from the Web of Science database. By applying filters for publication year, language, and document type, 590 documents were selected for analysis. Co-occurrence and co-authorship analyses were performed using VOSviewer to visualize the scholarly contributions and thematic developments. The study revealed notable publication growth, particularly after 2019. Prominent authors such as Rafael Valencia-Garcia and Francisco Garcia-Sanchez, along with leading institutions like the Chinese Academy of Sciences and Tsinghua University, were identified as major contributors. China stood out as the leading country in terms of publication numbers and citation impact. Ji Luo’s (2015) paper entitled "Transfer Learning Using Computational Intelligence: A Survey" emerged as the most cited work. Key areas of focus included natural language processing, information extraction, and deep learning, demonstrating the increasing influence of technological innovations on the field. This work provides a detailed bibliometric overview of text mining applications in knowledge management, highlighting significant trends, leading researchers, and core topics. It offers actionable insights for scholars and practitioners to navigate and contribute to this evolving area of study.

Abstract Today, it can be seen that in some organizations, succession management is recognized as a strategic process that is able to minimize the leadership gaps in key positions. Moreover, itprovides opportunities for the organization's competent and talented people to improve their necessary skills. The main purpose of the current research was to investigate the effect of the quality of training courses on the succession of directors based on the development of knowledge capital in educational organizations. This research study used a quantitative method and a survey approach.The statistical population of the study consisted of the principals of secondary schools of the Education Department of Shahre-h Qods. They completed the training courses in 2023. The results showed that the quality of training courses has a significant positive effect on organizational, individual, and contextual components of the succession of managers of the Education Department of Shahre-h Quds. Overall, the findings of this research emphasized the vital importance of high-quality training courses in strengthening the succession planning programs for education managers, as these courses play a key role in promoting the individual and organizational development, and improving the necessary foundations of succession.

Abstract In response to the increasing demand for luxury brands in recent years, managers and brand owners have concentrated on formulating and implementing strategies that meet the expectations of consumers. Literature in this field highlights that over the past few decades, the luxury brand sector has undergone remarkable transformations, evolving into a vital component of the global economy. These developments have introduced new objectives and policies for the international trade of luxury goods while simultaneously intensifying competition. Against this backdrop, the present study investigated how specific antecedents influence loyalty and online purchase intentions of luxury brands among women. Employing a descriptive–correlational design, the research study targeted a population of 1,707 female students at the Faculty of Humanities, University of Mohaghegh Ardabili, in 2024. Based on Cochran’s formula and convenience sampling, 145 students were selected to complete the research questionnaire. Data analysis was performed using SPSS and SmartPLS, applying the Partial Least Squares (PLS) technique. The findings revealed that consistency of brand concept, brand personality, and congruence of self-image—three central antecedents—exert a direct and significant impact on women’s loyalty to luxury brands and their intentions to purchase them online. These outcomes underscored the critical role of psychological and symbolic brand dimensions in fostering the customer loyalty and stimulating the online buying motivation.

Abstract Understanding and managing customer engagement are crucial in co-creating value and sustaining long-term customer relationships. This study develops a predictive segmentation model tailored to the banking sector, with a specific focus on emerging market contexts. Employing a mixed-methods approach, the research integrates a meta-synthesis of prior studies with a C5.0 decision tree algorithm to identify key engagement drivers. The novelty of the study lies in its integration of Relational Models Theory, Customer Lifecycle stages, and perceived emotional value into a unified predictive framework. A structured survey was administered to Iranian retail banking customers and the model segmented them based on their emotional and functional value perceptions, relational orientations, and lifecycle stages. Findings revealed that emotional value is the most influential predictor of engagement, followed by relationship stage and relational model type. Four distinct customer segments were identified, each with unique engagement profiles. The study offers practical tools for banks to personalize CRM strategies and optimize engagement efforts based on relational and behavioral insights. This research contributes to the literature by combining the relational theory and behavioral prediction within a service-dominant logic, offering actionable insights for banking institutions operating in culturally specific, emerging markets.