Journal of process management and new technologies

A DATA-DRIVEN MULTI-CRITERIA DECISION SUPPORT MODEL FOR ANALYZING SCIENTIFIC DEVELOPMENT OF OECD COUNTRIES

2026-06-23T06:36:43+02:00

In the global science ecosystem, countries' academic outputs are a key determinant of their strategic development level. The aim of this study is to examine the development dynamics of higher education systems over a period of approximately thirty years using integrated decision analytics. The scientific dynamics of OECD countries between 1996 and 2024 were analyzed using integrated LODECI and RAWEC methods based on the SCIMAGO database. The study compares long-term trends with current data from 2024 from a comparative perspective. The criterion weighting analysis conducted with LODECI determined that the Citations criterion is the most important determinant of academic competitiveness. This finding shows that the positioning of higher education institutions at the national and international levels is shaped in the context of scientific impact and productivity, beyond mere quantity. Countries were ranked according to their performance by integrating the weights into the RAWEC method. This study, which synthesizes innovative MCDM methods with long-term data, offers a unique methodological framework for analyzing countries' scientific development.

STRATEGIC INTEGRATION OF GASTRONOMIC HERITAGE IN GREEN TOURISM: AN EMPIRICAL STUDY FROM SERBIA

2026-05-26T21:59:01+02:00

Traditional gastronomy is an important part of intangible cultural heritage and plays an increasingly important role in the development of sustainable tourism. Bearing in mind the rise of interest in environmentally friendly tourism, traditional gastronomy may add to the ecological, socio-cultural, and economic sustainability of tourism destinations. In addition to cultural significance, traditional food products may also influence tourists’ attitudes toward environmentally friendly tourism. This study aims to investigate the role of traditional gastronomy in the green transition of tourism in the Republic of Serbia, with particular emphasis on tourists’ perceptions and intentions. A quantitative research approach, based on a structured survey of 95 tourists who have previously consumed traditional gastronomic products at various tourism destinations in the Republic of Serbia, was used. The respondents rated 32 items on a seven-point Likert scale. The results indicated high reliability since the internal consistency reliability coefficient for the instrument was high, with Cronbach’s alpha = 0.961. Descriptive analysis and Spearman’s rank correlation method were used to determine the relationship between gastronomic motivation factors and tourists’ intentions on green tourism. The results indicated a statistically significant positive relationship between gastronomic motivation factors and tourists’ intentions. This suggests that traditional gastronomy could be a major influence on tourists’ attitudes towards green tourism. Therefore, it shows the importance of incorporating gastronomic heritage in green tourism.

A FRAMEWORK FOR QUANTITATIVE CYBER RESILIENCE ASSESSMENT OF NETWORK ARCHITECTURES IN EDUCATIONAL INSTITUTIONS

2026-06-23T06:34:48+02:00

This paper investigates the development and application of an integrated framework for the quantitative assessment of cyber resilience of network architectures in educational institutions. Resilience assessment criteria were identified and mapped to the NIST Cybersecurity Framework to cover key functional areas of cybersecurity, while the ISO 31000 standard was applied to assess disruption scenarios, including cyber-attacks and network infrastructure failures. The PIPRECIA-S method enables precise weighting of criteria based on expert assessments, and the H-SCRM methodology allows quantitative evaluation of network alternatives according to defined criteria and scenarios. The results show that the hybrid cloud-managed network architecture achieves the highest level of cyber resilience, while software-defined networking and traditional LAN architecture achieve lower global resilience index values. Sensitivity analysis confirms the stability of the ranking of alternatives and the robustness of the proposed model. The paper provides practical guidelines for improving network security and decision-making in educational institutions through an integrated approach to risk and resilience management.

DIGITAL PLATFORMS ECOSYSTEMS AND BUSINESS MODEL DYNAMICS: AN EMPIRICAL ANALYSIS OF INTERNATIONAL AWARD-WINNING PROJECTS

2026-06-01T22:46:15+02:00

Digital transformation and digital platforms have led to significant changes in the way value is created, delivered, and captured in the economy and modern business. The aim of this paper is to analyze the structure of business models, monetization models, and the geographical scope of digital platforms awarded within the World Summit Awards (WSA) program in the period 2021–2025. The research is based on a mixed-method approach with a primary focus on qualitative analysis, applying content analysis and comparative analysis to a selected sample of 25 digital platforms. The results indicate the dominance of marketplace and fintech models, which account for more than two-thirds of the analyzed sample, while SaaS and AI platforms have a smaller but globally oriented presence. From a monetization perspective, transaction-based fees represent a significant revenue generation mechanism, while subscription models are primarily applied to standardized technological solutions. The findings reveal a clear correlation between the type of business model, monetization structure, and market expansion strategy. Interaction-based platforms tend to achieve regional growth, whereas standardized models demonstrate higher global adaptability. The contribution of this paper lies in the empirical analysis of platform development patterns across different environments, thereby enhancing the understanding of growth dynamics and internationalization of digital platforms, particularly in developing countries.

DATA-DRIVEN ORGANIZATIONAL CHANGE MANAGEMENT: INNOVATION, LEADERSHIP, COMMUNICATION, AND TEAMWORK

2026-06-13T11:35:32+02:00

This paper examines the determinants of organizational change within the contemporary business environment, with a particular focus on innovation, leadership, business communication, and teamwork. The objective is to theoretically conceptualize and empirically determine the key factors that drive the dynamics and success of corporate transformational processes. The theoretical findings indicate that innovation serves as the primary vehicle for competitive advantage, leadership acts as the critical catalyst for change, business communication constitutes the foundational platform for information exchange, and teamwork facilitates the collective execution of strategic goals. The empirical results confirm the high predictive power of the model (R²=0.982), with the level of innovation exerting the most substantial impact (β=0.416), followed by leadership (β =0.217), business communication (β=0.206), and teamwork (β=0.152). The study concludes that organizational change emerges from a synergistic relationship among the analyzed determinants, with innovative processes playing a dominant role.

A DECISION-SUPPORT FRAMEWORK FOR BANKRUPTCY PREDICTION USING EXPLAINABLE ENSEMBLE LEARNING

2026-06-14T13:05:05+02:00

Bankruptcy prediction is a challenging problem in the field of risk management of financial assets due to the rarity of bankruptcy events, the class imbalance that results from such rarity, and the regulatory requirements regarding the interpretability of AI-based decision systems. Given the gradual development of bankruptcy, it is necessary to use AI-based models that can capture non-linear relationships among financial metrics and detect early signs of issues in a company’s financial health. These requirements suggest using AI models beyond linear models and financial metrics alone. In this article, a Stacking Ensemble model is developed with both linear and non-linear models in order to investigate the ability of the models to predict bankruptcy with an emphasis on analyzing prediction trade-offs under severe class imbalance, as well as utilizing methods from the field of Explainable Artificial Intelligence (XAI) to investigate the models within the ensemble framework to determine the reasons for the ensemble’s performance on the evaluation metrics. Results indicate that the model has good discriminatory power, but is conservative in its decisions to recognize financial distress within companies. However, the requirement for model interpretability is still met, and the model’s performance across different evaluation thresholds is considered in the article.

SMART CONTRACTS FOR FINANCIAL SECURITY: PROCESS AUTOMATION, RISK MITIGATION, AND ORGANIZATIONAL IMPLICATIONS

2026-06-14T18:32:43+02:00

Smart contracts are one of the most prevalent and important blockchain-based technologies in the field of financial security, as the automatic execution of predefined rules provides additional efficiency, lowers costs and minimizes the involvement of intermediaries. This research will examine the use of smart contracts for process automation, risk reduction and organizational restructuring in the financial sector. In particular, the interaction between centralized and decentralized financial systems, the technology behind the implementation of blockchain and security issues associated with the use of smart contracts will be considered. At the same time, escrows will be presented as an example of the practical use of smart contracts for financial operations. The results of this analysis will show that smart contracts can be used as a means to increase the reliability of financial operations; however, their widespread use depends on proper regulation, security assessment and integration with the existing financial infrastructure.

BUSINESS PROCESS ANALYSIS OF STUDENT ACTIVITY FUNDING DISBURSEMENT IN A PRIVATE UNIVERSITY IN INDONESIA

2026-06-21T21:54:22+02:00

In addition to formal academic activities in university, student activities play an important role in developing students holistically; consequently, an adequate funding process is needed to ensure the smooth running of these activities. A study conducted at a private university in Indonesia showed that the funding disbursement process often involves multiple phases and actors/units and requires complex procedures. This results in inefficiencies, delays, and a lack of transparency. The goal of this study is to analyze the business process of funding disbursement at a private university in Indonesia and propose improvements to the existing business processes. This study used a qualitative approach in which data collection was conducted through semi-structured interviews with people involved in the process. The data was analyzed by mapping the business process based on Standard Operating Procedures (SOPs) and comparing it with the actual process. The findings of this study disclosed several weaknesses in the existing process, including a lack of digital system utilization, no standard processing time, a time-consuming verification phase, and the persistence of manual processes and documents. Business process improvement is then proposed, including a self-verification mechanism using mandatory checklists in a digital platform by student organizations, system automation, real-time status notification, and standardized processing times. To clarify the roles and responsibilities among stakeholders in the process, the Responsible Assignment Matrix (RACI) was developed. Through this process improvement, efficiency and transparency can be increased while a balance between control and operational effectiveness is still maintained.

BIBLIOMETRIC RESEARCH IN THE FIELD OF CYBERSECURITY: FOCUS ON ARTIFICIAL INTELLIGENCE AND ATTACK DETECTION

2026-06-21T22:22:38+02:00

This paper presents a comprehensive bibliometric analysis of the field of cybersecurity, with a specific focus on the application of artificial intelligence (AI) in attack detection. The study aims to provide insights into the trends, research areas, and key contributions in this rapidly evolving field, while also exploring the theoretical underpinnings of security and the challenges associated with the implementation of AI in attack detection. The integration of AI in cybersecurity has introduced significant advancements in detecting and mitigating cyber threats. However, it also poses several challenges, including the need for large datasets to train AI models, the complexity of accurately identifying novel and sophisticated attacks, and concerns regarding the transparency and interpretability of AI decisions. Theoretical frameworks in cybersecurity emphasize the importance of robust and adaptive defense mechanisms, which AI technologies strive to enhance. Despite these advancements, the dynamic nature of cyber threats necessitates continuous innovation and interdisciplinary collaboration. The search spanned publications from 2004 to 2024 and was performed on June 13, 2024. The findings of this bibliometric analysis provide valuable insights into the development and current state of research at the intersection of cybersecurity and AI. The study highlights the most influential works, prominent authors, and emerging trends in this critical area of study. Additionally, it shed light on the theoretical aspects of cybersecurity and the practical challenges faced in implementing AI for attack detection. This comprehensive analysis aims to serve as a valuable resource for researchers, practitioners, and policymakers, fostering a deeper understanding of how AI technologies can be effectively leveraged to enhance cybersecurity measures and address the complexities of modern cyber threats.