Journal of Management and Business Solutions

This study aimed to examine the effect of ESG disclosure quality on liquidity risk and financial flexibility, with emphasis on the moderating role of cash holdings among companies listed on the Tehran Stock Exchange. This applied study employed a descriptive-correlational research design based on panel data analysis. Based on theoretical foundations in sustainability reporting and financial structure literature, multiple regression models were developed. Data extracted from companies’ financial statements and management discussion reports were analyzed using the Generalized Least Squares (GLS) method. The findings indicated that improving the quality of sustainability and ESG disclosures contributes significantly to reducing liquidity risk and enhancing corporate financial stability by decreasing information asymmetry and improving stakeholder relationships. Furthermore, ESG-related risks, such as reputational risks, increase the likelihood of stakeholder sanctions and make external financing more costly. In this regard, cash holdings played a significant moderating role. Companies exposed to ESG-related risks increased their cash holdings based on a precautionary motive to cope with adverse financial shocks and maintain operational continuity. This cash buffer mitigated the effects of financial constraints and contributed to preserving financial flexibility. ESG disclosure quality is not merely a tool for increasing transparency; rather, it serves as a strategic mechanism for managing liquidity risk and enhancing financial flexibility. By adopting optimal cash holding policies, managers can mitigate the negative consequences of reputational risks and cash flow fluctuations and ensure corporate continuity and financial stability under conditions of economic uncertainty.

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.

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.

The present study was conducted with the aim of presenting a conceptual model for sustainability in barter business models, specifically goods and services barter in the consumer-to-consumer (C2C) domain, using a meta-synthesis approach. In terms of purpose, the study is exploratory, and in terms of approach, it is qualitative. The research method employed is meta-synthesis, which is used to integrate, analyze, and interpret the findings of previous studies. The research data were collected through a systematic review of valid scientific articles in the fields of barter and sustainability. Subsequently, using thematic analysis, the sustainability components of barter-based businesses were extracted and categorized. Finally, based on the results, a conceptual model of sustainability for barter-based businesses was developed. The results of data analysis indicate that various elements have been introduced in the scientific literature as sustainability components of barter-based businesses. In the present study, these components were extracted through a systematic review approach by examining scientific studies published between 2010 and 2026. The results show that components such as reducing the need for liquidity, reducing capital pressure, improving non-monetary cash flow, economic resilience under crisis conditions, optimal use of surplus inventory, reducing the risk of inventory accumulation, and reducing operational costs are discussed in the economic domain. In addition, factors such as extending product life, recirculation of goods, reducing waste and losses, reducing the environmental impacts of the supply chain, reducing pollutant emissions, and promoting environmental awareness have been discussed in relation to the environment and environmental issues. The results also indicate that components such as participation and voluntary cooperation, reducing social inequality, equitable access to goods and services, and critical consumer awareness in the institutional domain, as well as components such as the institutional legitimacy of barter-based businesses, social acceptance of barter, an organizational culture supportive of barter, and corporate social responsibility (CSR) in the domain of technology and infrastructure, have been identified in the scientific literature as sustainability components of barter-based businesses.

This study aimed to identify and examine the factors affecting brand image improvement in the automotive industry from the perspective of automobile customers in Tehran. This applied quantitative study was conducted using a descriptive-correlational, cross-sectional survey design. The statistical population consisted of automobile customers, owners, and potential buyers in Tehran who had experience with domestic or foreign automotive brands. The final sample included 384 participants selected through convenience sampling from automobile dealerships, after-sales service centers, automobile exhibitions, and online automotive customer communities. Data were collected using a structured researcher-made questionnaire consisting of demographic items and 48 items measuring product quality, safety and technical performance, design attractiveness, innovation and technology, price fairness, after-sales service quality, customer relationship management, advertising and communication effectiveness, social responsibility, brand trust, perceived value, customer satisfaction, and brand image improvement. The validity of the instrument was confirmed through expert review and pilot testing, and its reliability was supported by Cronbach’s alpha coefficients. Data were analyzed using SPSS and AMOS through descriptive statistics, exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. The inferential findings showed that the proposed measurement and structural models had acceptable fit indices. Exploratory factor analysis extracted 13 factors explaining 68.84% of the total variance. Confirmatory factor analysis confirmed the adequacy of the measurement model, with χ²/df = 1.54, GFI = 0.902, CFI = 0.953, TLI = 0.947, IFI = 0.955, RMSEA = 0.037, and SRMR = 0.041. Structural equation modeling showed that all examined factors had positive and significant effects on brand image improvement. The strongest predictors were brand trust, customer satisfaction, product quality, after-sales service quality, and safety and technical performance. The final model explained 74% of the variance in brand image improvement. The findings indicate that automotive brand image improvement is a multidimensional process shaped by trust, satisfaction, quality, technical reliability, service performance, perceived value, innovation, customer relationships, communication, design, price fairness, and social responsibility.

The first and most fundamental factor in corporate success is having successful managers with distinctive characteristics. Companies with powerful managers are better able to overcome forthcoming crises and make better decisions under risky conditions. When companies have powerful managers, they can better fulfill their responsibilities toward individuals and society. The purpose of this study was to examine the relationship between CEO power and corporate social responsibility, considering the moderating role of political connections. The statistical population of this study included all companies listed on the Tehran Stock Exchange during the period from 2015 to 2024, from which 117 companies were selected as the sample using the systematic elimination method. To analyze the data, multivariate regression using panel data techniques was applied. The results of the study indicate that there is a significant relationship between CEO power and corporate social responsibility. Moreover, political connections can influence the relationship between CEO power and corporate social responsibility.

Sustainability reporting is considered a key instrument for increasing transparency, promoting social responsibility, and improving organizational performance in the cosmetics and personal care industry. The purpose of this study was to identify the main components of sustainability reporting in this industry and to present an integrated framework. The research method was based on a systematic review of domestic and international studies, and the data were collected from scientific sources and relevant reports. The findings indicate that sustainability reporting is a multidimensional process influenced by the environment and legal requirements, organizational structure and management, the quality and transparency of financial reporting, and social responsibility and innovation. The results of the study present a four-component framework consisting of “environment and institutional framework,” “organizational structure and management,” “financial and sustainability performance and reporting,” and “social responsibility and innovation,” which can help companies operating in the cosmetics and personal care industry design and implement their sustainability policies and practices in a comprehensive and targeted manner. This framework also provides a basis for future research in the field of accounting and sustainability reporting.

In recent years, auditing services have undergone fundamental transformations due to the expansion of artificial intelligence technologies, machine learning, and artificial neural networks. At the same time, challenges related to customer trust, process transparency, and the interpretability of intelligent outputs have increasingly highlighted the need to design systematic and trust-based models. The purpose of the present study is to present a comprehensive model of artificial intelligence-based auditing services with a customer trust approach. This study falls within the category of mixed-method research and is exploratory–confirmatory in nature, conducted in two qualitative and quantitative phases. In the qualitative phase, library studies and semi-structured interviews with experts in auditing, financial management, and intelligent technologies were used to identify the dimensions, components, and initial relationships of the model. The statistical population of this section consisted of 10 specialists with relevant professional experience, selected through purposive sampling and the snowball method, and the data were analyzed using thematic analysis. In the quantitative phase, the statistical population included professional auditors and customers of auditing services, and the sample size was determined as 358 participants. The data collection instrument was a researcher-made questionnaire based on a five-point Likert scale. Descriptive statistics, structural equation modeling, and advanced machine learning-based analysis were used to analyze the data. The results showed that the key criteria for designing an artificial intelligence-oriented auditing model and the technical sub-criteria of intelligent auditing played the greatest role in explaining the benefits of auditing services and strengthening customer trust. Furthermore, the evaluation of fit indices and the results of machine learning models indicated desirable predictive accuracy and coherence of the final research model.