Journal of Management and Business Solutions

Detection of Fraud in Financial Statements Based on Managerial Tone Analysis, Financial Reporting Complexity, and Anomaly Detection Algorithms

Ali Jamali ^* iD

The present study aimed to identify fraudulent financial reporting based on managerial tone analysis, financial reporting complexity, and anomaly detection algorithms among companies listed on the Tehran Stock Exchange. This study was conducted using a quantitative applied research design with a correlational and explanatory approach based on machine learning and textual analysis techniques. The statistical population consisted of companies listed on the Tehran Stock Exchange between 2018 and 2024, from which 186 firms were selected using purposive screening criteria, resulting in 1302 firm-year observations. Data were collected from audited annual reports, financial statements, explanatory notes, and management discussion sections. Managerial tone was measured through sentiment analysis using natural language processing methods, while financial reporting complexity was assessed through readability indicators, report length, and disclosure structure measures. Fraudulent reporting risk was identified using abnormal accrual indicators and anomaly detection techniques including Isolation Forest, Local Outlier Factor, One-Class Support Vector Machine, Random Forest, and Autoencoder Neural Networks. Data analysis was conducted using Python, SPSS-27, and RapidMiner software through descriptive statistics, correlation analysis, logistic regression, ROC curve analysis, and machine learning model evaluation indicators including accuracy, precision, recall, F1-score, and AUC. The findings demonstrated that positive managerial tone (β = 0.31, p < 0.001), negative managerial tone (β = 0.36, p < 0.001), financial reporting complexity (β = 0.39, p < 0.001), Fog readability index (β = 0.28, p < 0.001), abnormal accruals (β = 0.47, p < 0.001), and Isolation Forest anomaly scores (β = 0.51, p < 0.001) significantly predicted fraudulent financial reporting. Correlation analysis revealed significant positive relationships among fraud risk, reporting complexity, abnormal accruals, and anomaly detection indicators (p < 0.01). Among the evaluated algorithms, the Autoencoder Neural Network demonstrated the highest predictive performance with an accuracy of 0.93 and AUC of 0.96, followed by Isolation Forest with an accuracy of 0.91 and AUC of 0.94. Machine learning-based anomaly detection techniques significantly outperformed traditional logistic regression models in identifying suspicious financial reporting patterns. The results indicate that integrating managerial tone analysis, financial reporting complexity indicators, and anomaly detection algorithms substantially improves the identification of fraudulent financial statements. The findings highlight the importance of combining textual analysis, artificial intelligence, and forensic accounting techniques within modern auditing and financial supervision systems. Furthermore, the superior performance of deep learning and anomaly detection models suggests that advanced computational technologies can significantly enhance fraud detection accuracy and support auditors and regulators in identifying hidden manipulation patterns within corporate financial disclosures.

Explaining the Impact of Investor Sentiment on Financial Decisions Considering the Role of Financial Constraints in Companies Listed on the Tehran Stock Exchange

Soroush Nourollahi iD , Nader Rezaei iD , Ali Jafari ^* iD , Asgar Pakmaram iD

This study aimed to explain the impact of investor sentiment on financial decisions considering the role of financial constraints in companies listed on the Tehran Stock Exchange during the period from 2018 to 2024. A total of 144 companies were examined as the statistical sample of the study. In terms of objective, this research is classified as an applied study, and in terms of inference method, it falls within the category of descriptive-correlational research. The results obtained from testing the research hypotheses using pooled regression and panel data regression methods in EViews version 10 at the 95% confidence level indicated that financial constraints do not have a significant effect on the relationship between investor sentiment and leverage ratio. Financial constraints have a positive and significant effect on the relationship between investor sentiment and debt maturity. Furthermore, financial constraints have a positive and significant effect on the relationship between investor sentiment and the speed of leverage adjustment. This study provides practical insights into the economic consequences of increased leverage during periods of high investor sentiment on firm value for investors. The present research enhances the understanding of the impact of investor sentiment on firms’ financial decisions.

Designing a Process Model of Intelligent Assessment Centers for the Establishment of a Meritocracy System in Government Organizations

Mohammadjavad Tajik iD , Mansoureh Moradi Haghighi ^* iD , Farshad Hajalian iD , Mehdi Amirmiandaragh iD

The present study aimed to design a process model of intelligent assessment centers for the establishment and strengthening of a meritocracy system in government organizations. This study was conducted using a qualitative approach with an applied-developmental orientation and an exploratory purpose. The methodological strategy was thematic analysis, which was employed to identify and conceptualize the dimensions and components of intelligent assessment centers in the public sector. The research participants consisted of 15 experts in the fields of human resource management, assessment centers, digital transformation, artificial intelligence applications, and public administration. Participants were selected through purposive and snowball sampling techniques based on their professional expertise and practical experience. Data were collected through semi-structured interviews and analyzed using a systematic thematic analysis process involving coding, categorization, theme development, and model construction. To enhance the rigor and trustworthiness of the findings, credibility, dependability, confirmability, and transferability procedures were employed throughout the research process. The findings resulted in the development of a comprehensive process model consisting of four overarching dimensions: input, process, output, and consequence. The input dimension included legal frameworks and administrative requirements, smart competency orientation, digital maturity and organizational readiness, and intelligent technologies and infrastructures. The process dimension comprised digital assessment process design, smart assessment methods and tools, data management and decision-support analytics, and validity, reliability, and ethical governance of assessment. The output dimension encompassed competency assessment results and analytical decision-support reports. The consequence dimension included feedback, development and continuous improvement of human capital, enhancement of human resource decision quality, promotion of meritocracy, reduction of subjectivity, increased organizational justice, and improvement of human resource governance quality. Overall, the model demonstrated that intelligent assessment centers function as integrated socio-technical systems that combine competency-based assessment, digital technologies, data-driven analytics, and governance mechanisms to support transparent, objective, and merit-based personnel decisions. The proposed model provides a comprehensive framework for implementing intelligent assessment centers in government organizations and offers a practical pathway for strengthening meritocracy through competency-based evaluation, algorithm-supported decision-making, transparent assessment procedures, and continuous human capital development. The integration of technological, organizational, ethical, and governance dimensions enables public organizations to improve the quality, fairness, accountability, and effectiveness of recruitment, promotion, appointment, and talent development processes.

The Impact of Total Quality Management with an Emphasis on Customer Orientation and Continuous Improvement on the Competitiveness of Nanotechnology Industries (A Case Study of Small and Medium-Sized Enterprises (SMEs))

Mohsen Shahsavani iD , Mahmoud Moeinadin ^* iD , Akram Taftiyan iD

The purpose of this study was to investigate the relationship between Total Quality Management (TQM) practices and the competitiveness of nanotechnology industries. Using Structural Equation Modeling (SEM), the study examined the causal relationships among Total Quality Management, customer orientation, continuous improvement, and competitiveness. The statistical population consisted of all manufacturing and service companies operating in the field of designing, producing, and distributing textile products across the country. The sample size was determined using Cochran’s formula and a simple random sampling method. A total of 500 questionnaires were distributed, and after excluding incomplete responses, data from 185 companies were analyzed, indicating a high level of reliability of the findings at the company level. Total Quality Management (TQM), based on the models proposed by Suciati et al. (2024) and Sun et al. (2022), was measured across five main dimensions, including leadership and top management support, employee training and participation, information and learning, process management, and customer focus, through 21 indicators. TQM performance from the perspective of customer orientation and continuous improvement was assessed using the models of Permana et al. (2019) and Megirang et al. (2023) through 10 indicators. Competitiveness was measured using the questionnaires developed by Perez-Brito and Bojorquez Zapata (2017) and Kafetzopoulos et al. (2015), across two dimensions of domestic and international competitiveness through 14 indicators. The reliability of the instrument was evaluated using Cronbach’s alpha coefficient, and the results confirmed the acceptable reliability of the questionnaire. Responses for all variables were recorded using a five-point Likert scale. The complex relationships among latent variables and the hypothesis testing process were analyzed using the advanced statistical technique of Structural Equation Modeling (SEM) to accurately examine both direct and indirect effects of the factors. The results indicated that all hypotheses exhibited positive and significant relationships. Customer orientation, continuous improvement, and competitiveness independently affected Total Quality Management. The strongest relationship was related to the effect of continuous improvement (0.46), while the weakest relationship was associated with the effect of competitiveness (0.34) on Total Quality Management. Furthermore, competitiveness, as the core variable, had a direct effect on customer orientation and continuous improvement. The findings suggest that integrating quality principles can contribute to operational excellence, process innovation, and sustainable growth in fields where product precision and reliability are critical.

The Effect of Internal Auditing on Net Value and Inflation Management in Iranian Companies

Younes Parvareh ^* iD , Peyman Sayadi iD , Leila Sadeghkhani iD , Sajad Beigi iD , Farimah Hossainkhani iD

This study investigates the multifaceted impact of internal auditing on corporate net value and its capacity to mitigate inflationary pressures in the context of Iran's persistent economic instability and chronic inflation. Integrating agency and institutional theoretical frameworks, the study synthesizes global and domestic empirical literature to establish a conceptual basis. The core of this study comprises an original empirical analysis utilizing a panel dataset of 142 firms listed on the Tehran Stock Exchange (TSE) spanning 2012 to 2023, totaling 1,704 firm-year observations. Through the application of fixed-effects panel regression and two-stage least squares (2SLS) instrumental variable estimation, the evidence demonstrates that superior internal audit function (IAF) quality is positively and significantly correlated with Tobin's Q (β=0.142,p<0.01β= 0.142,p<0.01) and economic value added (EVA) (β=0.098,p<0.05β=0.098,p<0.05). Furthermore, the interaction between IAF quality and inflation rates is found to yield positive effects on operating cash flow (β=0.187,p<0.01β=0.187,p<0.01) and stock returns (β=0.112,p<0.05 β=0.112,p<0.05), suggesting that a high-caliber IAF serves as a critical mechanism for neutralizing the adverse consequences of inflation. The study further establishes that earnings adjusted for inflation under IAS 29 are 23–31% lower than historical cost earnings, thereby underscoring the substantial materiality of inflationary distortions. These results offer vital implications for regulatory bodies, standard-setters, and practitioners of corporate governance in volatile economies."

Designing Productivity Outcomes of Fixed Asset Revaluation Based on Matrix Processes

Younes Hasanvand iD , Shahrokh Bozorgmehrian ^* iD , Allah Karam Salehi iD

The objective of this study is to design the productivity outcomes of fixed asset revaluation based on matrix processes. In terms of methodology, the study is exploratory and, through two qualitative and quantitative phases, seeks to achieve its analytical objectives by identifying emerging dimensions of the focal phenomenon and ranking the identified core dimensions within the context of petrochemical companies. In the qualitative phase, grounded theory analysis and interviews were employed, and through three stages of coding, a polygonal theoretical framework was developed. In the quantitative phase, based on a pairwise comparison matrix, the core components of the theoretical framework were examined in row “i” and column “j” to ultimately prioritize the core dimensions using matrix scoring percentages. The results obtained in the qualitative phase indicated that, based on conducting 14 interviews with qualified experts, a total of 208 open codes, 30 conceptual themes, 6 core components, and 3 structural categories were identified and presented within a polygonal framework. Furthermore, in the quantitative phase, the results indicated that the component of asset operational capacity is the most central productivity outcome resulting from the revaluation of fixed assets in petrochemical companies. This finding suggests that when asset values are revised based on real market conditions and current economic value, more accurate and realistic information becomes available to managers regarding production capacity, operational line capabilities, equipment depreciation, and cash flow generation power. Such information directly influences operational decision-making and enables more precise planning for maintenance, repair, and asset replacement.

Analysis of Capability–Attractiveness for the Application of Industry 4.0 Technologies in Smart Warehousing of Chain Stores with a Sustainability Approach.

Amirmohammad Shirani iD , Mansour Momeni iD , Fatemeh Saghafi ^* iD

Chain stores have expanded significantly. Warehouse management is one of the vital activities in the chain store industry; because performing it improperly can lead to delays in the supply and distribution of required goods, customer dissatisfaction, increased waste, the need for employee overtime, and increased operational costs. Therefore, it is evident that the use of technologies manifested in the Fourth Industrial Revolution significantly leads to saving time, energy, and costs; and in contrast, neglecting these technologies not only brings financial and operational losses, but can also lead to losing a competitive position in the market. An issue that has received less attention so far is the necessity of ranking and prioritizing the application of the technologies of this revolution based on their capability–attractiveness and scope of applications. Therefore, this research has been conducted with the aim of analyzing the capability–attractiveness of the application of Industry 4.0 technologies in smart warehousing of chain stores with a sustainability approach. A comprehensive analysis of the collected data from the expert community including academic specialists, artificial intelligence specialists, and software engineers was considered. Considering the deep information and knowledge of this group in the field of Industry 4.0 technologies and smart warehousing of chain stores, their views were considered vital for weighting the indicators. For this purpose, specialized questionnaires were distributed among this panel in order to record and collect their opinions regarding the “attractiveness” and “capability” indicators. In the analysis section, first the weights of the indicators were determined using the Best–Worst Method. Then, the various applications of Industry 4.0 technologies in smart warehousing of chain stores were evaluated using the VIKOR ranking method. The results show that the possibility of searching for and finding a specific item in the warehouse in the shortest possible time and the automatic registration of inventory reduction of goods from a shelf or pallet in the warehouse system indicate high attractiveness and capability. Also, the possibility of being aware of the most suitable location for placing goods based on the latest layout indicates low attractiveness and high capability. Also, access to real-time warehouse inventory, elimination of human errors during loading and shipping of goods, optimization of warehouse space considering awareness of the optimal order quantity of goods indicate high attractiveness but low capability. Also, accurate inventory counting in the shortest time, automatic recording of inspection, maintenance and repair data, intelligent connection of machinery and equipment to each other in warehouse management, awareness of the exact inventory of warehouse shelves or pallets at the time of placement indicate low attractiveness and capability.

Developing a Good Human Resource Governance Framework in Public Holding Companies: The Case of the MAPNA Group

Parisa Dehghani ^* iD , Ali Pirannejad iD , Abbas Nargesian iD , Habibollah TaherpourKalantari iD

Human resource governance is rooted in corporate governance and focuses on risk management, compliance, and business success. Developing a human resource governance framework capable of safeguarding entry points and high-risk areas through the involvement of various actors and the use of appropriate mechanisms is the primary objective that this study seeks to achieve. This study is qualitative in nature and employs a systematic literature review and a multiple case study approach using homogeneity analysis. Initially, studies relevant to the research questions were examined, and primary themes were identified. Subsequently, interviews were conducted through purposive sampling, and their themes were extracted. Using homogeneity analysis, the two sets of themes obtained from the literature review and the case study were compared, leading to the development of the proposed model of the study. Following the conducted analyses, 35 concepts were identified and categorized into 14 subcategories and 4 main categories. The human resource governance framework was formulated, and its principles and actors were identified. The four main categories include governance domains, governance actors, governance rules and reports, and principles of human resource governance. Based on the conducted interviews, concepts such as monitoring and reporting in human resource governance, actors involved in human resource governance, domains of concern in human resource governance, and transparency and accountability as principles of human resource governance constitute the components of the human resource governance framework. Organizational culture was also identified as one of the elements incorporated into the framework.

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