Journal of Management and Business Solutions

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.

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

Ali Jamali ^* iD

https://doi.org/10.61838/jmbs.323

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.

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."

Developing a Supply Chain Model Based on an Effective Marketing-Mix Approach to Consumer Behavior in the Automotive Industry: A Qualitative Thematic Analysis

Mostafa Hakemifar iD , Babak Hajikarimi ^* iD , Abdollah Nazari iD

This study aimed to develop an integrated supply-chain model based on an effective marketing-mix approach to explain consumer behavior in the automotive industry. This applied qualitative study constituted the exploratory phase of a mixed-methods research project. Data were collected through semi-structured interviews with 12 experts in automotive supply-chain management, marketing, production, distribution, logistics, and after-sales services in Iran. Participants were selected through purposive and criterion-based sampling, and interviews continued until data saturation was reached. The interview transcripts were imported into MAXQDA 2020 and analyzed using thematic network analysis. Meaning units were identified, coded, compared, and organized into basic, organizing, and global themes. Credibility was enhanced through repeated review of the transcripts, expert consultation, constant comparison, analytical memoing, and maintenance of an audit trail. The analysis generated four interconnected supply-chain blocks: suppliers, manufacturers, distributors, and logistics and support services. In the supplier block, the principal themes were quality, price, timely delivery of parts, innovation, and communication. The manufacturer block comprised quality, timely delivery, price, innovation, and collaboration. The distributor block included after-sales services, a strong distribution network, collaboration with manufacturers, and experience and expertise. The logistics and support block consisted of experience and training, effective communication, coordination capability, technology use, and compliance with standards. The findings indicated that consumer behavior is influenced by the combined effects of product quality, price fairness, delivery reliability, innovation, information transparency, distribution accessibility, technical support, and after-sales service. The themes also showed that weaknesses in one supply-chain block can reduce the effectiveness of the entire marketing mix and negatively affect perceived value, satisfaction, purchasing intention, and customer loyalty. Effective management of consumer behavior in the automotive industry requires the strategic integration of marketing-mix decisions with coordinated supplier, manufacturing, distribution, logistics, technological, and service capabilities.

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.

Artificial Intelligence–Driven Demand Forecasting and Inventory Performance in Omnichannel Supply Chains: The Contingent Roles of Data Quality, Demand Volatility, and Organizational Analytics Maturity

Shahab Atashzay ^* iD

This study examined the effect of artificial intelligence–driven demand forecasting on inventory performance in omnichannel supply chains and tested the moderating roles of data quality, demand volatility, and organizational analytics maturity. A quantitative cross-sectional study was conducted among 312 managers, supervisors, analysts, and specialists employed in omnichannel organizations in Tehran, Iran. Data were collected using a structured questionnaire measuring artificial intelligence–driven demand forecasting, inventory performance, data quality, demand volatility, and organizational analytics maturity. The measurement model and structural relationships were analyzed using partial least squares structural equation modeling with 5,000 bootstrap resamples. Reliability, convergent validity, discriminant validity, multicollinearity, explanatory power, predictive relevance, effect sizes, and interaction effects were assessed. Artificial intelligence–driven demand forecasting had a significant positive effect on inventory performance (β = 0.421, p < 0.001). Data quality (β = 0.263, p < 0.001) and organizational analytics maturity (β = 0.196, p < 0.001) positively predicted inventory performance, whereas demand volatility had a significant negative effect (β = −0.148, p < 0.001). Data quality strengthened the forecasting–performance relationship (β = 0.163, p < 0.001), while demand volatility weakened it (β = −0.121, p = 0.001). Organizational analytics maturity also amplified the positive effect of artificial intelligence forecasting (β = 0.142, p = 0.001). The final model explained 61.2% of the variance in inventory performance and demonstrated acceptable predictive relevance and fit. Artificial intelligence–driven demand forecasting improves inventory performance in omnichannel supply chains, but its benefits are greatest when organizations possess high-quality integrated data and mature analytics capabilities and are reduced under highly volatile demand conditions.

A Context-Specific Model for Transforming Payame Noor University into an Entrepreneurial University: Identification and Prioritization of Factors Affecting Human Resource Performance Using Fuzzy Delphi–AHP and BSC Methods

Nazanin Ghasemkhani iD , Kambiz Peykarjou ^* iD , Lotfali Agheli iD

This study aimed to comparatively investigate and analyze the role of social-class disparity and life expectancy in health expenditure in Iran, Germany, and France. The study employed a descriptive-analytical and comparative research design and incorporated both quantitative and qualitative data. The quantitative data included indicators of social-class disparity, life expectancy, healthcare-system quality, per capita income, health policies, and health literacy over the period from 2015 to 2025. These data were analyzed using panel regression models and correlation analysis. The qualitative data were collected through content analysis of official documents, international and national reports, and the health policies of the three countries. The findings indicated that increases in social-class disparity and life expectancy lead to higher health expenditure; however, the role and effect of social-class disparity were greater in Iran than in Germany and France. In developed countries such as Germany and France, extensive insurance coverage, preventive policies, and improvements in the quality of healthcare services have reduced the financial burden on households and facilitated better management of health expenditure. Iran continues to experience rising healthcare costs because of resource constraints and inequalities in access to healthcare services, although policies such as universal health insurance and the Health Transformation Plan have alleviated some of these problems.

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