International Journal of Business Management and Entrepreneurship

Developing a Fuzzy Network DEA Model with Linking Constraints to Evaluate Overall and Stage-Level Efficiency of Supply Chains under Uncertainty: An Application in Automotive Manufacturing

2026-04-27T14:21:55+0330

Traditional data envelopment analysis (DEA) models for assessing supply chain efficiency treat stages independently, ignore links between them, and overlook data uncertainty. This study presents a fuzzy network data envelopment analysis (FNDEA) model to assess the overall efficiency of a three-stage supply chain (supplier, manufacturer, and distributor) under uncertainty. Input, output, and intermediate product variables are modeled as triangular fuzzy numbers. To solve the fuzzy model, the α-cut approach is used, transforming it into two deterministic linear programs with lower (optimistic) and upper (pessimistic) bounds. By applying compatibility constraints on the flow of intermediate products, the model simultaneously accounts for dependencies between stages and presents the efficiency of each stage and the overall efficiency of the chain as intervals. The model is implemented on data from 10 real supply chains, and efficiency is calculated at five α-cut levels of 0, 0.25, 0.5, 0.75, and 1. The results show that the proposed model identifies inefficient steps and provides improvement paths at different levels of uncertainty. This approach allows managers to base decisions on efficiency intervals rather than definite numbers and to conduct more robust analyses in uncertain environments.

Institutional Development Approaches in Arts Universities Adapting to Changing Public Policy Environments

2026-05-15T13:40:55+0330

Arts universities worldwide are operating in increasingly turbulent public policy environments marked by declining public funding, intensified demands for measurable employability and economic impact, rapid digital and technological transformation mandates, sustainability imperatives, and evolving frameworks for equity, diversity, and inclusion. These shifting policies create both existential threats and strategic opportunities for specialized higher education institutions dedicated to the arts. This study investigates the institutional development approaches that arts universities employ to adapt proactively while preserving their distinctive artistic experimentation, critical pedagogy, and cultural missions. Employing an explanatory sequential mixed-methods design, the research first surveyed 278 deans, institutional leaders, and senior administrators from 112 arts universities across 22 countries (response rate 61 %) between 2021 and 2025. This quantitative phase was followed by 38 purposive in-depth case studies involving 112 semi-structured interviews, analysis of 94 strategic and policy documents, and selective site observations. The findings reveal that successful adaptation hinges on three mutually reinforcing institutional development approaches: (1) adaptive and resilient organizational models that emphasize distributed leadership and problem-driven iterative adaptation (PDIA); (2) comprehensive whole-institution strategies that integrate curriculum reform, operational sustainability, digital innovation, and multi-stakeholder co-creation; and (3) sophisticated strategic boundary-spanning that enables leaders to align core artistic values with external policy priorities such as creative economy growth, cultural diplomacy, and social cohesion. Institutions that effectively implemented these approaches demonstrated significantly higher levels of organizational resilience—including greater enrollment stability, diversified revenue streams, innovation outputs, and policy influence—alongside improved graduate outcomes and sustained artistic integrity. The study proposes a conceptual framework for institutional development in arts higher education that enables balanced navigation of external pressures and internal mission preservation. Results emphasize that adaptation should be viewed as a deliberate, values-driven strategic process rather than short-term compliance. Without such sophisticated institutional development, arts universities risk progressive marginalization; through it, they can reinforce their role as vital anchor institutions for cultural vitality, societal innovation, and sustainable development in the twenty-first century.

AI-Driven Fraud Detection in Financial Statements: A Comparative Study of Machine Learning Models

2026-07-03T23:28:55+0330

The increasing complexity of financial transactions and the growing volume of corporate data have significantly intensified the risk of financial statement fraud, challenging the effectiveness of traditional detection methods. This study examines the role of artificial intelligence in enhancing fraud detection by conducting a comparative analysis of machine learning and deep learning models applied to financial statement data. Drawing on an extensive review of prior literature and empirical findings, the research evaluates the performance of widely used algorithms, including logistic regression, decision trees, support vector machines, random forest, gradient boosting, and deep learning architectures. The results indicate that ensemble and deep learning models consistently outperform conventional statistical and rule-based approaches in terms of accuracy, precision, and overall predictive capability. However, the findings also highlight critical challenges related to data imbalance, model interpretability, and computational complexity, which may limit the practical adoption of advanced models in real-world auditing and regulatory contexts. The study contributes to the existing literature by providing a structured comparison of AI-driven fraud detection techniques and offers practical implications for auditors, regulators, and policymakers seeking to improve the reliability and transparency of financial reporting. Future research directions include the development of explainable and hybrid models, the integration of non-financial and textual data, and the expansion of cross-country datasets to enhance model generalizability and support sustainable capital market development.

Credit Risk Prediction in Digital Banking Using Hybrid Machine Learning Techniques

2026-07-30T12:36:06+0330

Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs. Machine Learning (ML) models are also being used to make predictions in these applications. Accurate forecasting requires knowledge of these machine learning models and their distinguishing features. The datasets we use as input for each of these different types of ML models, yielding different results. The choice of an ML model for a dataset is critical. A loan risk model is used to show how ML models for a dataset can be linked together. The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk. This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields. If credit risk is considered, a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried. Various approaches to credit risk calculation will be examined, ranging from the most basic to the most complex. In addition, we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches, logistic regression, decision tree, and gradient boost to see which one produced the most commercially useful insights.

Internal Audit Independence and Corporate Governance: Effects on Financial Performance

2026-07-30T12:54:01+0330

Internal audit independence and effective corporate governance are widely recognized as critical mechanisms for enhancing organizational accountability and financial performance. This study examines the relationship between internal audit independence, corporate governance structures, and firm financial performance. Using an empirical research design, the analysis investigates how the autonomy of internal audit functions measured through reporting lines, organizational status, and freedom from managerial influence interacts with key corporate governance attributes such as board independence, audit committee effectiveness, and ownership structure. The findings indicate that higher levels of internal audit independence are positively associated with improved financial performance, reflected in stronger profitability, efficiency, and risk management outcomes. Moreover, the results suggest that corporate governance mechanisms play a moderating role, strengthening the impact of internal audit independence on firm performance when governance structures are robust. The study contributes to the literature by providing evidence on the complementary relationship between internal auditing and corporate governance and offers practical implications for boards, regulators, and policymakers seeking to enhance financial performance through stronger governance and independent assurance functions.

Improving the Self-Efficacy of Claims Department Employees in Insurance Companies and Its Role in the Effect of Electronic Monitoring on Job Performance

2026-07-30T13:34:38+0330

In the digital age, to boost performance and achieve high-quality development, an increasing number of companies have begun implementing electronic monitoring of performance. However, existing studies have produced conflicting results regarding the impact of electronic monitoring on employees’ job performance. To clarify the overall relationship between these variables and examine the underlying causes of these discrepancies, this study—based on social information processing theory—investigates the effects of two types of electronic monitoring on employees’ job performance (developmental and preventive) and the mediating role of self-efficacy. This descriptive–survey study was conducted using a validated questionnaire on 110 employees of insurance companies in Fars province, sampled from a population of 153 across 22 companies. SmartPLS 3 software was used to test the model and hypotheses. The findings indicate that both developmental and preventive electronic monitoring have a positive effect on the job performance of claims department employees in insurance companies, and employees’ self-efficacy serves as an important mediator in this relationship.

The Effect of Artificial Intelligence on Enhancing Customer Experience through the Mediation of Social Media Marketing (Case: Iranian Tourism Platforms)

2026-02-07T14:55:00+0330

In today's digital era, particularly within the tourism industry, the use of social media marketing tools such as chatbots, virtual influencers, and augmented reality (AR) has emerged as one of the most effective strategies for enhancing user engagement and satisfaction. This study aims to elucidate the role of Perceived Artificial Intelligence (PAI)-based technologies in improving customer experience and to examine their impact through social media marketing instruments. The research population comprises customers of Iranian tourism platforms. Data were collected via a structured questionnaire, with items explicitly designed to measure the dimensions of Perceived AI (perceived usefulness, perceived ease of use, perceived enjoyment, perceived intelligence, and personalization), and analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique with SmartPLS software. A total of 367 questionnaires were evaluated. The findings indicate that specific dimensions of Perceived AI are significantly associated with social media marketing components (chatbots, augmented reality, and virtual influencers), which in turn show a meaningful relationship with customer experience. The results underscore the pivotal role of Perceived AI dimensions in enhancing customer experience on tourism platforms, a role that is notably amplified through the use of social media marketing tools. Accordingly, it is recommended that platform managers strategically and intelligently integrate Perceived AI technologies alongside social media marketing instruments to optimize customer engagement and satisfaction

Investigating the Effect of Organizational Justice on Job Satisfaction and Organizational Citizenship Behavior, with the Mediation of Organizational Commitment (Case: Bank Mellat)

2026-02-25T00:09:59+0330

Organizational justice, defined as employees' perception of fairness across distributive, procedural, interactional, and informational dimensions, plays a pivotal role in shaping organizational attitudes and behaviors. In service-oriented organizations such as banks, where human interactions and employee commitment are critical to success, moderate levels of organizational justice, organizational commitment, job satisfaction, and organizational citizenship behavior (OCB) have led to challenges including reduced productivity, high turnover rates, and diminished service quality. This study examines the relationships among organizational justice (independent variable), organizational commitment (mediating variable), job satisfaction, and OCB within Bank Mellat's head offices in Tehran, aiming to propose strategies for enhancing organizational culture and performance. The research adopted a positivist, quantitative survey approach. The population comprised over 1,230 employees at Bank Mellat's central offices in Tehran, with a stratified sample of 293 participants selected. Data were collected using standardized questionnaires: Colquitt's scale for organizational justice, Allen and Meyer's for organizational commitment, Miner's for job satisfaction, and Podsakoff's for OCB. Data analysis was performed via partial least squares structural equation modeling (PLS-SEM) using SmartPLS 4 software, yielding excellent model fit indices (GOF = 0.72, SRMR = 0.047, NFI = 0.93). All hypotheses were supported. Organizational justice exerted a significant positive indirect effect on job satisfaction through full mediation by organizational commitment. Direct effects were also strong: organizational justice on commitment, job satisfaction, and OCB; and commitment on job satisfaction and OCB. Explained variances were substantial. These findings underscore the critical importance of fostering organizational justice to enhance employee commitment and improve key organizational outcomes in the banking sector.

Transformational Leadership and Climate of Trust: The Mediating Role of Organizational Justice in Mellat Bank

2026-02-09T13:12:42+0330

Despite widespread agreement on the positive outcomes of an organizational climate of trust, its antecedents have received comparatively limited scholarly attention. The present study examines the effect of transformational leadership on the climate of trust through the mediating role of organizational justice at Mellat Bank. This applied research employed a descriptive survey methodology. The statistical population consisted of employees from the Planning and Transformation Department and the Human Capital Department of Mellat Bank. Using stratified sampling, 274 employees were selected. Data were collected through standardized questionnaires and analyzed using Structural Equation Modeling (SEM). The results indicate that transformational leadership has a positive and significant effect on both organizational justice and the climate of trust. Organizational justice also has a significant positive effect on the climate of trust and mediates the relationship between transformational leadership and the climate of trust. Strengthening transformational leadership alongside enhancing organizational justice can therefore serve as an effective strategy for developing a climate of trust in organizations.

Personalized Fraud Detection in Online Banking Using Sequential Pattern Mining and Machine Learning

2026-02-01T14:22:04+0330

With the rapid expansion of online banking and the increase in financial transactions on digital platforms, fraud detection in these systems has become a major challenge for financial institutions. Traditional fraud detection methods, which are typically based on predefined rules, are unable to cope with new threats due to the complexity and diversity of frauds and the rapid changes in user behavior. In this paper, a personalized approach for fraud detection in online banking using sequential pattern mining and machine learning is proposed. In this approach, each user's financial transactions are analyzed as time sequences to identify the normal behavioral patterns of each user and extract suspicious deviations. Advanced algorithms like PrefixSpan are used for extracting sequential patterns, and machine learning algorithms such as Random Forest and SVM are employed to classify transactions as fraudulent or non-fraudulent. Experimental results show that the proposed model outperforms traditional methods, with higher accuracy and better ability to detect complex frauds. This research can contribute to improving fraud detection systems in online banking and enhancing the security of financial transactions.

Root Cause Analysis of the Fire Incident at Shahid Rajaee Port Using the Fishbone (Ishikawa) Method: A Case Study from Bandar Abbas, Iran

2026-01-27T21:16:49+0330

The fire and explosion at Shahid Rajaee Port in April 2025 represent a critical industrial incident with significant human, operational, financial, and environmental consequences. This study applies the Fishbone (Ishikawa) method to systematically identify the root causes of the event, based on interviews with port operators, safety experts, and emergency responders. Six main categories of contributing factors were examined: Human, Machine/Equipment, Materials, Methods/Processes, Environment, and Management. Findings reveal that inadequate staff training, fatigue, and poor communication, combined with equipment failures, improper hazardous material storage, weak operational procedures, adverse environmental conditions, and a weak safety culture, collectively created the conditions for the disaster. The analysis underscores that such incidents are rarely the result of a single failure but emerge from the interaction of multiple systemic vulnerabilities. Based on these findings, the study recommends continuous workforce training, systematic preventive maintenance, rigorous hazardous material management, standardized operational and emergency procedures, infrastructure improvements, and strengthened organizational governance to enhance resilience and reduce the likelihood of similar events. This research provides a comprehensive framework for port authorities and policymakers to implement integrated risk management strategies, emphasizing the need for a holistic approach that addresses technical, human, and organizational dimensions of safety.

Providing a social media marketing model to increase return on investment in the Tehran dairy industry

2026-02-15T14:15:03+0330

Through the use of a mixed (qualitative-quantitative) approach, this study aimed to provide a social media marketing model to increase return on investment in the Tehran dairy industry. The statistical population in the qualitative part of the study included experts of Tehran diary industry. Targeted sampling was used as sampling method in this part, which continued till theoretical saturation was achieved. In the quantitative part, the statistical population included the customers of Tehran diary Industry, who were 384 people and were selected using Cochran's formula. Semi-structured interview and Likert scale questionnaire were used to collect data. For data analysis in the qualitative part, grounded theory was used to identify the indicators of the social media marketing model through MAXQDA software. Then, the initial model was designed using structural-interpretive modeling and MICMAC software. Structural equation modeling and LISREL software were used to validate and present the final model. The research findings indicated that the components of knowledge management, customer needs’ assessment and customer engagement are the underlying components of the model that affect strategic marketing, competitive advantage, financial and non-financial performance as well as the return of the investment and lead to media effectiveness and customer loyalty.