Journal of Management and Business Solutions

CEO Characteristics and Stock Price Crash Risk: A Meta-Analysis Approach

Ghazaleh Barzegar iD , Ahmad Ahmadi ^* iD , Habibollah Nakhaei iD , Karim Nakhaei iD

Purpose: This study employs a meta-analytic approach to synthesize and reconcile the inconsistent empirical evidence regarding the association between CEO characteristics and stock price crash risk. Specifically, the analysis focuses on five key CEO attributes: overconfidence, managerial ability, power, age, and gender.

Method: Following the meta-analytic framework proposed by Lipsey and Wilson (2001), effect sizes were extracted and synthesized from 57 empirical studies, comprising 19 domestic studies published between 2015 and 2023 and 38 international studies published between 2015 and 2025. Stock price crash risk served as the dependent variable, while CEO characteristics constituted the independent variables. The robustness and validity of the meta-analytic estimates were assessed using Cochran's Q test for heterogeneity, Egger's regression test for publication bias, leave-one-out sensitivity analysis, and other conventional meta-analytic diagnostic procedures.

Findings: The results reveal significant positive associations between CEO overconfidence, CEO power, and CEO gender and stock price crash risk. In contrast, no statistically significant relationship is observed between managerial ability or CEO age and stock price crash risk.

Conclusion: The meta-analytic evidence demonstrates that CEO-specific behavioral and governance attributes—particularly overconfidence, power, and gender—constitute important determinants of stock price crash risk. These findings underscore the importance of incorporating CEO characteristics into corporate risk assessment, corporate governance mechanisms, executive selection processes, and investment decision-making, thereby contributing to improved risk management and greater informational efficiency in capital markets.

Developing a Model for Promoting the Circular Economy in the Country’s Manufacturing Industries from an Islamic Economics Perspective

Amin Qobadi iD , Fatemeh Zandi ^* iD , Parvaneh Salatin iD

The present study was conducted with the aim of developing a model for promoting the circular economy in the country’s manufacturing industries from an Islamic economics perspective. In terms of purpose, this study is applied; in terms of approach, it is developmental-exploratory; and in terms of research type, it is qualitative. The study population consisted of a group of experts, including university faculty members, senior managers of manufacturing industries, and experienced economic consultants. Using theoretical sampling, individuals were selected from among these experts as study participants and interviewed. The selection of experts and the interviewing process continued until theoretical saturation was reached, after which data collection was terminated. Ultimately, 12 experts were interviewed. The approach adopted for analyzing the interviews was thematic analysis. The findings ultimately resulted in the development of a model for promoting the circular economy in the country’s manufacturing industries from an Islamic economics perspective, comprising six global themes and 21 organizing themes. According to the resulting model, organizational and environmental factors, as the primary drivers, influence the strategic factors required for realizing the circular economy. These strategic factors, in turn, facilitate the realization of the circular economy, ultimately generating a set of organizational and societal outcomes.

A Relationship Marketing Model Based on National Social Media

Abolfazl Taheri iD , Saeid Eslami ^* iD , Reza Eslami iD

The present study aimed to develop a relationship marketing model based on national social media. In terms of purpose, this study was applied, and in terms of the data collection process, it was a descriptive and field study employing both qualitative and quantitative approaches. The participants in the qualitative phase consisted of experts familiar with the research topic. A minimum of 10 participants was initially considered for structured interviews, with sampling continuing until theoretical saturation was reached; ultimately, 25 participants were included in this phase. The statistical population of the quantitative phase consisted of marketing experts and managers working in food manufacturing companies. Using simple random sampling and Cochran’s formula, the sample size was determined to be 384 participants. The qualitative data were analyzed using MAXQDA 24 software, while the quantitative data were analyzed through structural equation modeling (SEM). Based on the analyses, five main dimensions were identified. Branding comprised brand awareness, brand image, brand equity, brand development, brand experience creation, and brand identity. Content marketing comprised education, content production, guidelines and instructions, differentiation, appropriate content, mental engagement, and audience building. Information technology comprised software and hardware, human resources, support networks, deployment, effective utilization, and technology localization. Advertising comprised search engines, value creation, support, information dissemination, and innovation. Interaction comprised public relations, criticisms and suggestions, feedback acquisition, motivation, sharing, membership, and loyalty. The overall fit of the research model was highly satisfactory and was therefore confirmed.

Designing a Qualitative Model of Value Chain Governance for the Development of Knowledge-Based Companies in the Tourism Industry

Jafar Jangi iD , Mohammad Feizi ^* iD , Vahid Araei iD

The primary objective of this study was to design a value chain governance model for the development of knowledge-based companies in the tourism industry using a qualitative approach. From a paradigmatic perspective, the study was grounded in an interpretive approach and employed thematic analysis to explore and provide an in-depth explanation of the dimensions, components, and underlying mechanisms of value chain governance within the context of tourism knowledge-based companies. The study population consisted of academic and executive experts. Using purposive sampling and the principle of theoretical saturation, in-depth semi-structured interviews were conducted with 20 participants. The resulting data were coded and analyzed using Braun and Clarke’s (2006) thematic analysis method in MAXQDA software. To enhance the credibility and reliability of the findings, strategies including peer review, coding–recoding, and calculation of Cohen’s kappa coefficient were employed. The final findings led to the identification of a structure comprising 180 basic codes, 35 organizing themes, and nine global themes: value chain architecture, value chain policymaking, interactive regulation, value-oriented culture, information integration, communication integration, continuous value optimization, value-creation dynamism, and sustainable tourism development. These themes were integrated into a comprehensive conceptual model depicting the mechanisms of value chain governance and the pathways through which they influence the sustainable development of tourism knowledge-based companies.

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.

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.

Designing a Model for Developing a Strategic Public Relations and Crisis Communication Planning Document for the National Iranian Gas Company: A Qualitative Approach

Farhad Yousefi iD , Mehran Samadi ^* iD , Mandana Saniee iD

This study aimed to design a qualitative model for developing a strategic public relations and crisis communication planning document tailored to the organizational conditions and communication requirements of the National Iranian Gas Company. This applied qualitative study was conducted with 21 participants, including senior managers and decision-makers of the National Iranian Gas Company, public relations and organizational communication specialists, crisis management experts, and university faculty members. Participants were selected through purposive and snowball sampling, and data collection continued until thematic saturation was achieved. Data were gathered through in-depth semi-structured interviews and supplementary analysis of relevant organizational documents. Interviews lasted approximately 50–90 minutes, were recorded with participants’ consent, transcribed verbatim, and analyzed using inductive thematic analysis. MAXQDA software was used to organize, code, compare, and categorize the data. Credibility, dependability, confirmability, and transferability were strengthened through member checking, peer debriefing, audit-trail documentation, reflective memoing, and review of the preliminary model by subject-matter experts. Analysis of the data yielded 684 preliminary codes, which were reduced to 247 final open codes and organized into 49 subcategories, 15 main categories, and five overarching dimensions. The resulting dimensions were strategic foundations and environmental orientation, governance and organizational capacity, integrated stakeholder and media communication, crisis communication lifecycle management, and evaluation, learning, and strategic improvement. The findings indicated that effective crisis communication planning requires alignment between communication strategy and corporate mission, clear decision-making authority, executive sponsorship, interdepartmental coordination, stakeholder segmentation, integrated media and digital communication, scenario-based preparedness, unified messaging, rumor management, post-crisis reputation recovery, and continuous performance evaluation. The model further demonstrated that crisis communication should be embedded within routine public relations processes and supported by a cyclical feedback mechanism linking evaluation results to strategic revision, training, and resource allocation. The proposed model provides an integrated strategic and operational framework through which the National Iranian Gas Company can strengthen communication governance, crisis preparedness, stakeholder trust, media credibility, organizational coordination, and post-crisis learning.

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