محمد تقی تقوی فرد

 Business model change in startups is essential for adapting to evolving market demands and increasing competitiveness, playing a critical role in their success. However, most research related to business model change has mainly focused on established firms. To address this research gap, the present study provides a framework that contributes to understanding the role of design themes in startup business model changes. In this study, a systematic literature review was used to analyze theoretical foundations and prior researches. After defining the research question, designing a search strategy, and applying screening criteria, 95 articles published between 2000 and 2024 were reviewed. In the first stage, a conceptual model was developed to analyze the dimensions of business model change by reviewing theoretical foundations and prior researches. This model was then refined through inductive-deductive thematic analysis, using evidence from empirical articles and case studies. The identified themes—novelty, efficiency, lock-in, complementarity, and adaptability—were examined across four main dimensions of business model change: content, structure, governance, and stream. The findings showed these themes interact synergistically and contribute to competitive advantage and business sustainability. The research results suggest that these five overarching themes provide a suitable framework for understanding and managing business model change in startups.

We are on the verge of a paradigm shift in how businesses are organized. The service ecosystem has provided many opportunities to design and innovate business models in a network environment. The purpose of this research is to provide a service ecosystem framework for the collaboration of a network of heterogeneous organizations for mobility as a service. In this framework, organizations should be able to create new value services together that each of them could not provide alone. The type of this research is qualitative and based on a case study. To provide an initial framework, thematic analysis was per- formed using MAXQDA software and coding was performed on the research literature. To collect evidence, judgmental sampling was used to collect opinions from knowledgeable individuals in academia, government, and industry. Professional documentation and sup- plementary reports were also used to create a chain of evidence. The statistical popula- tion consisted of 16 individuals related to the research topic, and the data collection tool was a semi-structured interview. By presenting the proposed framework, it is possible to integrate resources and coordinate different and diverse services to a traveler in a service ecosystem based on a data ecosystem and a shared data space. This ecosystem includes three main networks with the titles of value network for theorizing, developing a com- mon institutional logic and providing a solution to reduce obstacles and challenges of the ecosystem, governance network for decision-making, guidance and coordination for par- ticipation and cooperation of companies and structural network for integrating resources based on ecosystem actors.

Due to the speed and uncertainty of environmental changes, firms become less confident in the effectiveness of planning that is based on previous experience and assumptions. Instead, executives started to recognize that business decisions should not only be driven by past data, but also be based on systematic evaluation of possible forthcoming trends by constantly scanning for and interpreting discontinuities in the external environment. Corporate foresight can be defined as the desire of the company to understand futures for the long-term interests of the company. This article was written with the aim of a systematic review of corporate foresight literature between 2006 and 2022. The systematic review of corporate foresight literature includes the scientific research works of countries, the distribution of articles based on the year of publication, journals, authors, the number of citations to the journals and researchers and classified from the prospective of methodology. The findings show that the subject of corporate foresight has received more attention over time, and researchers can use different theoretical and methodological approaches to investigate corporate foresight and cover the research gap.

Designing service compensation strategies is one of the sensitive, complex and challenging issues in human resource management in recent decades. Its requirement is to achieve strategic reference points and the aim of the research is to identify the dimensions and components that influence on the compensation strategies of managers in the automotive industry. The research method is qualitative in nature and practical in terms of purpose. The number of participants based on saturation The opinion was determined by the managers of automobile companies with more than ten years of work experience in this industry, and the information was collected and analyzed through semi-structured interviews with 14 experts who were selected according to the researcher's judgment. The theme was used to analyze and interpret the data from the interview. The findings of this research include eight organizing themes of basic salary (fixed payment), special salary (variable payment), mixed payments, allowances (income in addition to salary), Short-term and long-term rewards, benefits (special managerial facilities), managerial knowledge and decision-making skills, and the ability to recognize issues and solve problems are effective reference points in compensation strategies for managers in the automobile industry.

There are many indicators and criteria in the effectiveness of skill training so that the trained person can enter the labor. These indicators and sub-indices should have an operational aspect and be weighted and ranked based on modern methods. The purpose of this study is to identify the weight and the rank of these criteria so they can be used in codification and evaluation of educational and skill models. The statistical population of the research consists of 7 managers and administrators of industrial companies, school managers of skills training and experts in the field of developing training courses, technical courses. These people reviewed the research works and interviews at the first place. The indicators and criteria of the research have been identified with experts. Then, using Fuzzy Delphi method, 5 main indicators and 12 sub-indicators have been approved, and at the end, with the fuzzy best -worst method, the ranking of indicators and sub-indicators were calculated. The results show that the criterion of education alignment with employment status is the most important and main indicator. Also, the criterion of participation of business owners in curriculum planning is the most important sub-indicator of the effectiveness of skill training.

Regional Electric companies are organizations that pursue both social and financial goals in order to fulfill their assigned missions, so fulfilling accountability for their dual goals is of fundamental importance. In this research, by examining the information needs of the users of the financial reports, the factors affecting the financial reporting of the sector have been studied. The aim of this research is to present a model for the environmental conditions and characteristics of regional electric companies in Iran. For this purpose, the required data, after a library study and exploratory search in the theoretical foundations and financial and accounting rules and regulations governing the relations of these entities, were collected using a questionnaire and analyzed using the fuzzy Delphi research method and appropriate statistical tests. The evidence from the analysis of the respondents’ views shows that the influencing factors are in four dimensions, including the compatibility of the model in achieving the organization's goals, the needs of information users, compliance with financial and accounting laws and regulations, and finally, budget control and credit status reporting. Experts also agree on the factors proposed by this research for designing and explaining the financial reporting model of regional power companies in Iran.

The government and other public sector institutions provide many goods and services essential to society. One of the most significant areas in which the government engages in commercial activities is the energy sector, particularly the electricity industry, which plays a key role in social and economic development and in improving the quality of life in society. The financial resources of the country’s regional electricity companies are derived from various sources and must be utilized for specific purposes in compliance with various laws and regulations. Given the fundamental importance of performance and the evaluation of accountability by authorities to stakeholders in democratic governments, it is necessary to produce clear and reliable reports on how resources are allocated and consumed in different areas. This research seeks to identify the weaknesses and shortcomings of the financial statements of these institutions by reviewing existing literature and employing other methods. Ultimately, the study presents a suitable model for the financial reporting of these institutions, enabling improved accountability and more effective economic decision-making.

What is the financial reporting model of Iran's regional electricity companies?

Sayedi and Babajani (2011) conducted research with the aim of achieving a desirable model of financial reporting in Islamic capital markets. The findings of their research confirmed the usefulness of relying on the fund theory to achieve reporting goals. The result of their study led to a financial reporting model in which the central focus is not the economic entity but rather the main business activities and departments of the company. Babajani and Shekarkhah (2012), using the Delphi method, investigated the appropriate accounting model for Islamic banking in Iran. According to their findings, the accounting system used by experts was unable to meet user needs and required changes, the generalities of which were presented as key factors in the proposed research model. Additionally, the research indicated that a conceptual framework based on accountability is more suitable than one based on decision-usefulness for financial reporting in the usury-free banking system. Mohammadi et al. (2020), in their research to develop a desirable model of financial reporting in the public sector, addressed the key factors influencing public sector financial reporting. The results showed that the quality of public sector financial reporting is influenced by factors such as budgeting structure, public sector infrastructure, the quality of human resources, the accounting system and basis, education, and the accountability system governing society. Weaknesses identified in the research include poor implementation of accrual accounting, incomplete implementation of operational budgeting, political influence in decision-making processes, lack of access to the annual budget performance statement and budget deduction report, and non-identification of certain financial statement items.

The current research is an applied and developmental study. It aims to analyze existing conditions and assist in the decision-making process, classifying it as descriptive research. Furthermore, since it seeks to gather the opinions of a large statistical community on the research subject, it is categorized as descriptive-survey research.
Using the fuzzy Delphi method, the statistical population includes experts selected based on three criteria: the presence of representatives from expert groups, deep knowledge of the research topic, and breadth of opinion and expertise. These experts were chosen from groups such as auditors, the head of the State Court of Audit, auditors of executive bodies, directors of audit organizations, and academic faculty members with relevant experience. Participants were selected through snowball sampling. Initially, to identify the dimensions, components, and indicators of the Financial Reporting Model for Regional Electric Companies in Iran, a review of the subject literature was conducted. After identification, examples of these cases in the public sector were examined based on expert opinions. Given the advantages of the fuzzy Delphi method (29FDM) compared to the traditional Delphi method (30TDM), the fuzzy Delphi method was employed in this research.

By examining the needs of users and the current state of financial reporting for regional electric companies, which is affected by several factors, it was found that the financial reporting models of private sector for-profit institutions cannot fully address the information needs of users in this sector. The findings of this research revealed that the operating environment and user needs in this sector differ significantly from those of private-sector for-profit institutions. These differences have created challenges in aligning the information needs of users with the form and content of the current financial reports, leaving report users facing difficulties.
In this research, by identifying and analyzing the factors influencing an appropriate financial reporting model for Iran's regional electric companies, a step has been taken to better meet the information needs of users. Identifying the set of influencing factors for the proposed model, in accordance with the conditions and laws governing the financial and accounting systems of this sector, and determining the importance of each factor in the desired framework are among the key findings of this research effort.

According to the findings of the research, the consistency of the model in reporting the degree of achievement of the set goals of regional electric companies, the consistency in supporting the goals of information users, its compliance with financial and accounting rules and regulations, as well as budget control and credit status reporting, were identified as the effective dimensions of the reporting model. Each of these dimensions comprises several components and indicators.

This research aimed to present a financial reporting model for Regional Electric Companies in Iran. Using the fuzzy Delphi method and based on expert opinions, the dimensions, components, and indicators of the proposed model were identified and presented.

Urban development and the increase in intercity and intracity transportation have led to a significant rise in transportation costs. This rise in cost increases the final product price and consequently the product's market price. Additionally, the economic aspect is not the only concern; increased transportation results in higher CO2 emissions. These issues have driven the design of various transportation and vehicle routing models, including the vehicle location-routing problem. Facility location is a strategic decision due to the high costs associated with constructing and locating facilities. On the other hand, routing decisions depend on location decisions and are considered mid-term and short-term decisions.In this study, a mathematical model for vehicle location-routing under uncertainty conditions and considering reliability is developed to simultaneously optimize sustainability objective functions while maintaining minimum reliability levels. The sustainability objectives in this study include minimizing total costs, minimizing CO2 emissions, and maximizing job opportunities based on integrated strategic and tactical decisions. The primary focus of this paper is decision-making for optimal transportation routing, considering time windows and the reliability of facility location based on their failure rates.

Given the uncertainty of the mathematical model parameters, various fuzzy and robust possibilistic programming methods were employed to formulate the model. Four different models were used to manage the uncertainty of demand parameters and transportation costs, and the results were compared. Additionally, two methods—an exact solution approach and a meta-heuristic algorithm—were employed to solve the multi-objective mathematical model. The enhanced epsilon constraint method was used to analyze small-scale mathematical models and conduct sensitivity analyses, while the NSGA-II algorithm was applied to solve larger-scale numerical examples. Furthermore, an initial solution based on prioritization was utilized for the meta-heuristic algorithm.

The results indicate that although an increase in uncertainty rates leads to more job opportunities, it also raises total costs and greenhouse gas emissions. Additionally, the analysis reveals that the RPP-III method achieves the highest model robustness cost with the lowest standard deviation. In the reliability analysis, it was observed that higher facility failure rates result in an increased number of located production and distribution centers. This, in turn, leads to higher total costs, increased CO2 emissions, and more job opportunities. By analyzing 15 numerical examples, it was also found that NSGA-II is highly efficient in solving the mathematical model compared to the enhanced epsilon constraint method.

The findings of this research assist managers in making strategic decisions such as facility location and tactical decisions such as vehicle routing in the context of market uncertainty. Given that the model incorporates various realistic decisions and assumptions, the mathematical model can be effectively applied in distribution industries, particularly for pharmaceutical and electronic goods.

The ever-increasing development of digital technologies has brought about significant changes in business performance. The increase in the number of published articles on this topic also shows the special attention of researchers in information systems, business management, and innovation. While digital changes are inevitable in the digital age, previous research has been limited to a specific domain. This research aims to identify key themes and macro topics through a systematic review of 201 articles from 2018 to 2023 through two high-quality databases (Scopus and Web of Science). First, using thematic analysis, the main themes are identified, and their relationships are investigated from the perspective of digital technology development. In the next step, by using topic modeling (Latent Dirichlet Allocation), the major domains of the impact of these technologies will be investigated, and future research trends will be identified using the scientometric approach. The innovation of this research is designing a thematic network through in-depth text review and text mining analysis, which leads to a better understanding of the relationships between critical components. In the last step, recommendations are given to researchers and managers to conduct future research.

This research seeks to provide a model for explaining the dimensions of customer sentiment management of asset management services, including investment funds and portfolio-managed accounts, using meta-synthesis application. By reviewing and screening 254 papers written between 2002-2022, coding was done by studying 49 studies. Finally, the components were classified with 32 final codes as basic themes and 5 codes as organizing themes. The reliability and validity of the model were calculated by a Kappa coefficient equal to 0.78, which is at the level of excellent agreement. Since this study uses the meta-synthesis method, its innovation is a method that examines all national and international studies in this field and identifies the dimensions of the subject completely and comprehensively for the readers. According to the results, the components of customer sentiment management dimensions of asset management services were categorized with five themes including the importance of sentiment management, influencing factors on customer sentiments, sentiment characteristics, sentiment management practices, and results of sentiment management. The results can be used by managers of asset management institutions in designing strategies for managing customers' sentiments.

This research aims to develop a three-dimensional approach for evaluating and segmenting suppliers in the online retail market, using a combined qualitative and quantitative approach.

In this research, a mixed methodology based on four different methods including Meta-synthesis, Thematic analysis, Fuzzy Delphi, and Confirmatory Factor Analysis has been employed.

In this paper, a systematic review of research in the field of supplier segmentation was conducted using the meta-synthesis method. Ninety-six criteria were extracted and categorized into economic, social, and environmental components. Subsequently, content analysis was employed to gather opinions from other supply chain members, such as suppliers and customers. To adapt and customize the criteria to the nature of online retail activities, a Fuzzy Delphi method was used. The hierarchical model for evaluating and segmenting suppliers of dairy products in the Digikala online store was then presented. Furthermore, the proposed research model was evaluated and validated using the factor analysis method.

The results of data analysis in two qualitative and quantitative phases of the research indicated that, according to experts’ opinions, the hierarchical model of sustainable development is the most appropriate model for the evaluation and segmentation of dairy food suppliers in the online retailing market. In conclusion, 17 criteria framed within the economic, social, and environmental dimensions of sustainable development were introduced as components and criteria for evaluating and segmenting Digikala’s dairy food suppliers.

Crowdfunding is an effective way to achieve the non-profit goals of the charities which can be expanded using information and communication technology. In this regard, this study was conducted with the aim of providing an online donation intention model for collective financing of charitable institutions in Iran. The current research is an applied-developmental research in terms of its purpose, and it is considered a cross-sectional survey research from the data collection point of view. The statistical population in the qualitative section includes managers of charity institutions and university professors. Sampling was done using a purposeful method and theoretical saturation with 10 interviews. In the quantitative section, the views of 357 benefactors were used. The data collection tool is a semi-structured interview and a researcher-made questionnaire. To analyze the collected data, qualitative thematic analysis and partial least squares were used. The research findings showed that the technical infrastructure facilitating conditions, the quality and transparency of the website information, the security of the site and the application, and the preservation of privacy affect the perceived risk. Perceived risk affects the expectation of effort and the pleasure of helping others and leads to the expectation of performance and social influence. Finally, online donation intention is strengthened through trust.

The main objective of this research is to study the relationship between corporate default and key investment strategies in the Iranian stock market. These strategies are value strategies and continuation of past performance (momentum). The concept of market anomalies, including value, size, and momentum premium, has been studied mainly in the stock exchange of the United States or other countries. Research focusing exclusively on the Tehran Stock Exchange is limited and related to the past years. Considering Iran's influential role in the region's economy and its damage from the recent global crises, it will be important to answer whether there are abnormal returns resulting from value premium, size premium and continuation of past returns on the Tehran Stock Exchange. Another goal of this research is to establish and investigate the theoretical relationship between corporate default and investment strategies.

In this research, using four corporate default indicators, including the probability of default calculated by Campbell, Hilscher and Szilagyi (2008) (named as CHS score), the criterion based on Merton model (1974) (named as M score), Altman's Z score (1968) and Ohlson's O score (1980), the role of this factor in pricing capital assets, as well in explaining the profitability of investment strategies known as value premium, size premium and momentum premium, is investigated and analyzed. In this way, a total of 10 models are used and tested: (1) Fama-French three-factor model (1993), (2) Carhart four-factor model (1997), (3) augmented Fama-French model including CHS score, (4) augmented Carhart model including CHS score, (5) augmented Fama-French model including Z score, (6) augmented Carhart model including Z score, (7) augmented Fama-French model including O score, (8) augmented Carhart model including O score, (9) augmented Fama-French model including M score, and (10) augmented Carhart model including M score. This research uses data from 168 companies listed on the Tehran Stock Exchange and Iran Over the Counter Market over 114 months.

According to this research, including the corporate default factor in the Fama-French three-factor model (1993) and Carhart four-factor model (1997) improves the performance of these models in pricing stocks. In the meantime, the indicator of the probability of default calculated by Campbell, Hilscher and Szilagyi (2008) has the greatest contribution to increasing the explanatory power of the mentioned models. After that, Ohlson's O score (1980) and Altman's Z score (1968) are ranked second and third, and the measure based on Merton's model (1974) stands at the last place. Additionally, it is found that value premium, while indifferent to the presence or absence of the corporate default factor in asset pricing models, is mainly seen among small and big value stocks. After augmenting these models with the corporate default factor, the significance of the size effect disappears among big value stocks. However, this variable still maintains its influence in explaining the fluctuations of returns of three other stock portfolios, including small value stocks, small growth stocks and big growth stocks. In addition, the findings show that the excess returns of none of the stock portfolios contain momentum premiums. More importantly, the research results suggest that the probability of corporate default and the excess returns of stock portfolios are significantly negatively correlated.

According to the results, the first hypothesis of the research, “the corporate default factor explains the stock returns of firms listed on the Tehran Stock Exchange and Iran Fara Bourse,” is confirmed. The second hypothesis of the research, based on which “including the corporate default factor improves the performance of Fama-French three-factor model (1993) and Carhart four-factor model (1997) in the pricing of capital assets”, is also confirmed. The third hypothesis of the research, according to which “the corporate default factor changes the effect of risk factors related to size, book value to market value and momentum in Fama-French three-factor model (1993) and Carhart four-factor model (1997)”, is confirmed about size and value effects. Still, there is no evidence that with the inclusion of the corporate default factor in Carhart's four-factor model (1997), the impact of the momentum variable on the excess returns of stock portfolios will increase or decrease. The fourth research hypothesis, that “market-based corporate default criteria have more power in explaining the profitability of value premium, size premium, and momentum premium, compared to the criteria based on accounting data”, is not fully confirmed or rejected. According to the above findings, the effects of value, size, and momentum are not compensation for corporate default risk. It is suggested that individual and institutional investors in the stock market pay attention to the financial health of the firm, its size, and its book to market ratio, respectively, instead of focusing on the past performance of its stocks.

The current research was conducted to design and model digital transformation based on the fourth-generation industrial revolution in Iran Khodro Company. In this regard, the main question focuses on identifying the factors that impact the design and modeling of digital transformation, rooted in the fourth-generation industrial revolution, and exploring the pattern of causal relationships among these factors in Iran Khodro. 

This study adopts an applied-developmental approach in terms of its purpose, utilizes a descriptive survey method for data collection, and employs a mixed data analysis method. The community of participants in the qualitative section comprised of experts, policymakers, academics, managers of knowledge-based companies, and senior managers of Iran Khodro Company. Sampling was done in a targeted way and theoretical saturation was achieved with 15 people. The statistical population for the quantitative part comprised experts from Iran Khodro Company, estimated to be 172 individuals using Cochran's formula. The required sample volume was provided by a simple random sampling method. A semi-structured interview and a researcher-made questionnaire were used to collect data. The underlying categories of digital transformation in Iran Khodro Company were identified by the grounded theory method and the final model was validated by the partial least squares method. Qualitative data analysis was conducted using Maxqda software, while the quantitative analysis was performed using Smart PLS.

To explain the digital transformation model based on the fourth-generation industrial revolution in Iran Khodro company, semi-structured expert interviews were conducted with marketing professors and managers of Iran Khodro company. At this stage, before the start of the interview, six open questions were considered and during the interview process, new questions were also raised as expected. To get familiar with the depth and scope of the content of the data, repeated and active reading of the data (searching for meanings and patterns) were done. The results of the interviews were extracted using the grounded theory method based on the theory of Strauss and Corbin and by open, central, and selective coding. In the open coding stage, 256 codes were first identified, duplicate codes were removed and synonyms were combined, and finally 58 open codes were identified. During the central coding stage, the initial 58 codes were categorized into 10 classes, and in the selective coding stage, these 10 classes were further condensed into six main classes. In the process of theorizing the database, after collecting, analyzing, and interpreting the data, the next step is to present the model, draw conclusions, and summarize the research. By examining the current situation, the obtained data were classified into six main categories. Professors and experts contributed their perspectives, leading to the utilization of 58 indicators to elucidate the digital transformation model rooted in the fourth-generation industrial revolution at Iran Khodro Company, drawn from all indicators derived through qualitative analysis of interview content.

Based on the research paradigm model, it was determined that digital infrastructure, digital leadership, digital human resources, and digital marketing affect digital transformation in Iran Khodro Company. The fourth industrial revolution provides the necessary platform and the competitiveness of Iran Khodro plays an intervening role. Finally, the digital strategy of Iran Khodro Company leads to the financial and non-financial performance of the company.

Today, one of the most important challenges of non-governmental organizations is financing. Due to the lack of government funding, these organizations use public assistance to continue their activities and realize their charitable goals and visions. With the increasing use of online donation platforms in the world, non-governmental organizations in Iran have not yet been able to use this opportunity to solve their financial challenges. This is an important issue that needs to be investigated and researched. The purpose of this article is to provide a model to explain the factors affecting the willingness to donate online in line with the collective financing of non-governmental organizations, which has been carried out using a Meta-synthesis method. In this regard, using the experiences of financial managers and charity experts and previous research, the collected information was coded using the content analysis method. The components were categorized with 55 descriptive open codes, 36 basic themes and 12 organizing themes, then the reliability and validity of the model was checked and then it was presented in the form of the final model. The results showed that these components are directly and indirectly effective in online donation in line with collective financing of non-governmental organizations. These components clearly state that providing transparent and timely financial information and effective activities of non-governmental organizations, emotional and social support for people, ease of use, privacy protection, trust and the principle of the plan and project is the right way. which can make the financing of non-governmental organizations successful.

Customers now care more than ever about the sustainability and reliability of products packaging. This research considers a multi-product and multi-period closed-loop supply chain design problem with three objectives of profitability, social responsibility, and reliability under uncertain conditions. Triangular fuzzy numbers have been used for non-deterministic parameters and a robust probabilistic programming approach with Me scale has been used to deal with fuzzy constraints. The proposed approach eliminates the need for iterative consideration by decision-makers by providing unlimited choices from the optimism-pessimism spectrum. The mathematical model developed in this study is of mixed integer linear programming type, which is implemented Augmented Epsilon Constraint (AEC) method in GAMS software to solve it and find Pareto optimal solutions. The accuracy of the overall performance of the proposed model has been evaluated with four examples (based on the coefficients of the objective functions) from a case study in the Carton-Making Industry. The obtained results indicate the existence of a conflict between the three objective functions. With this account, decision-makers should demand lower profits for increased environmental protection and improved reliability compared to the situation where only the economic aspect is considered. The variability of the justified decision space in the Me criterion has helped to solve the supply chain network design problem more flexibly and closer to reality through the possibility of exchange between the objective function and the risk-taking level of the managers.

In this article, following a review of the background and literature related to development banking, an examination of the key and influential factors in development banking is conducted. The documents and resources selection criteria included the alignment of title, abstract, and content with development banks. The statistical population of this research includes available articles & resources related to development banks which are available in domestic and foreign databases, websites, and the statutes of development banks. All documents related to the subject until 2023 AD and 1402 SH were examined. Using the CASP method, 149 articles and documents were evaluated, and ultimately, a targeted selection of 85 articles/documents was subjected. 88 components, 8 themes, and 6 categories were identified, and their reliability was confirmed through Cohen's kappa coefficient. Long-term investment and financing of projects, focusing on industry and production sectors, focus on large companies, the management approach, knowledge acquisition and innovation from advanced countries, focusing on infrastructure sectors, comprehensive risk management, and improving capital structure were among the most important and recurring identified components. As this research is the first article on the meta-synthesis of factors affecting development banking performance, it provides a deeper understanding of the existing literature on these banks.

Subject and Purpose of the Article:

 Regional electricity companies are organizations that pursue both social goals and financial goals in order to fulfill the assigned missions, so fulfilling and evaluating the responsibility of accountability due to their dual goals is of fundamental importance. In this research, the weaknesses and inadequacies of the financial statements have been studied by examining the information needs of the users of the financial reports of these companies.

The data has been collected after library study and exploratory search in the theoretical foundations and financial and auditing laws and regulations of these companies and using questionnaires and has been analyzed using appropriate statistical.

The evidence from the analysis of the views of the respondents shows that the information provided in the financial statements is not sufficient for the purpose of fulfilling and evaluating the responsibility of accountability, which is a priority, and there is a significant gap between the current situation and the desired situation. The findings indicate that in addition to financial information regarding the organization as a whole, users also need information regarding the financial status, financial and budget performance of each organization's goals.

Conclusion, Originality and its Contribution to the Knowledge: 

According to the findings of the research, the users use the financial statements of regional electricity companies with the aim of evaluating the responsibility of accountability, it is necessary to design and implement a suitable reporting model to improve accountability.

Evaluation of service quality is one of the main concerns of government and non-government service providers. With the advancement of mobile technologies and the provision of government services to citizens in the framework of mobile government, the evaluation of the quality of this type of service has already received the attention of governments and researchers in the field of mobile government service quality. In this research, thematic analysis method and interpretive structural modeling (ISM) technique have been used. By analyzing the theme of the identified factors and themes and using the interpretive structural modeling method, the factors have been identified and classified among these factors.The method of theme analysis and interpretive structural modeling is based on the opinion of experts. The experts in this research consist of 14 expert professors, researchers and executives in the field of mobile government. The results of the research show that evaluating the quality of mobile government services at the first level, satisfying citizens at the second level, educating citizens and building trust in mobile government services at the third level, designing aapplication at the fourth level, data security and the culture of mobile government services at the fifth level, organizational policy, Web3 capabilities, and software and hardware infrastructure are at level six.

The present study aimed to develop a model for determining an optimal bidding strategy for electricity producers, including the recommended selling price and the amount of electricity to be offered for participation in both the competitive electricity market and the energy exchange market. Hourly bids are suggested for the electricity market, while a monthly package, comprising peak load, medium load, low load, and base load, is proposed for the exchange market. By modeling a self-scheduling problem, the study aimed to develop optimal power production plans that maximize net profit over a one-month period. The research approach involved mathematical modeling using mixed-integer non-linear programming, which was performed in Lingo software and then validated in terms of effectiveness through an application to the case of a thermal power station. Relying on fuzzy necessity, credibility, and possibility, the research presented a robust model against the uncertainty of price with an adjustable level of robustness. Sensitivity analysis and the simulation approach were used to validate the performance of the model, demonstrating that the optimal response from the robust model, compared to the deterministic model, can maintain its efficiency in the face of fluctuations in the parameter of price uncertainty. Furthermore, the findings indicated that offering a base load package on the energy exchange market can yield a higher net profit value for the producer. Finally, the fuzzy interest rate and decision-making based on fuzzy goals were also examined.

In today's rapidly changing global landscape, the success of an organization hinges significantly on the harmonization of all its components in pursuit of its strategic objectives. Ensuring alignment between information technology (IT) and the organization's overarching strategies is paramount for the effective operation of any entity. What adds particular importance to this endeavor is the comprehensiveness of the organization's IT strategy. Given the substantial investments that numerous healthcare institutions, including social security hospitals, make in information technology, it is evident that IT is deemed a strategic resource of utmost significance, essential for its strategic utilization. To investigate the impact of IT on the strategic positioning of social security hospitals and assess the degree of alignment between IT strategies and organizational strategies, a comprehensive research study was undertaken concerning the social security hospitals within Zanjan province. The research sample for the social security organization was determined through the administration of questionnaires, and the reliability coefficient (Cronbach's alpha) was calculated using SPSS software. For data analysis, both descriptive and inferential statistics were employed, facilitated by the utilization of both SPSS and PLS software. The research findings reveal that the social security organization has reached a level-Four maturity in terms of strategic alignment between IT and the business. However, to elevate the maturity level of strategic alignment within this organization, it is imperative to adopt a strategic and holistic approach, entailing a progression towards a level beyond the Fifth stage of strategic alignment. Achieving this goal necessitates the formulation of specific and tailored strategies. In summary, factors such as IT strategy alignment, organizational strategy coherence, maturity level assessment, strategic alignment, and proactive strategic planning are all critical components that must be carefully considered to ensure the success and resilience of any organization within the current dynamic and fiercely competitive business environment.

The competitive environment of today has focused the attention of organizations on meeting the requirements of quality and social responsibility. Because organizations that adhere to the quality management framework achieve a higher level of customer satisfaction. To consider the convergence between sustainability and reliability, an economic, responsible, and reliable supply chain network design model has been comprehensively and efficiently modeled. In this article, a non-linear mixed integer programming model is considered for the design problem of the supply chain network in the form of three objectives, multi-product, multi-level, multi-source, multi-capacity, and multi-stage. The Pareto optimal solutions of the proposed model have been obtained using the evolved epsilon constraint (AEC) method. In addition, a numerical example with random data has been used to measure the accuracy and overall performance of the proposed model. The results showed that the profit value of the problem increases with the increase of the demand parameter. At the same time, reliability and social responsibility decrease. In addition, with the increase in greenhouse gas emissions, the value of the objective function of profit and social responsibility decreases, while the value of the objective function of reliability remains almost constant. Achieving synergy between sustainability and reliability in the design of the supply chain network requires the development of more detailed and long-term guidelines.

Fire insurance is a type of insurance that provides financial protection against property damage caused by events such as fire, theft, and weather-related damage. The use of modern analytics methods in analyzing policies and predicting potential losses is of great interest to insurance companies and those involved in property insurance services. Claim prediction is a crucial measure for predicting future losses in insurance companies, particularly in property insurance, where it helps evaluate the Probable Maximum Loss (PML) based on risk theory.

This study focuses on developing claim prediction models for occurrence probability, severity, and time by creating a dataset, training various algorithms, and comparing their performance. Exploratory Data Analysis (EDA) is conducted to select relevant features, resulting in the selection of 44 fields from insurance policy and claim information. Recursive Field Elimination (RFE) is used to reduce the dataset dimensions, and informative fields are selected for each prediction model. The dataset consists of over 780,000 policy records and approximately 70,000 loss payments from the Iran Insurance Company’s actual fire application database spanning 10 years (2011-2021). Linear regression, random forest regression (RFR), support vector regression (SVR), and deep neural network algorithms are implemented for each prediction model. The accuracy of the algorithms is evaluated using mean squared error (MSE) and mean absolute error (MAE) values.

The results of the prediction models show that the deep multi-layer perception (MLP) algorithm performs the best. After hyperparameter tuning and multiple runs, the final MSE values are determined as 0.117 for occurrence probability prediction, 0.042 for loss severity prediction, and 0.106 for claim time prediction. The comparison of the innovative model results with test data demonstrates that intelligent models provide more accurate predictions. Accurate prediction is highly valuable for insurance companies as it allows them to mitigate financial losses. The use of deep learning in loss time prediction, in addition to severity and probability prediction, is a significant innovation in this research.

Machine learning techniques, such as deep learning, can help insurance companies optimize their services with higher accuracy, strengthen risk management practices, and provide tools for better decision-making. The application of deep learning in loss prediction can replace complex and inaccurate manual processes in insurance, leading to advancements in risk management, reinsurance management, and improvement of fire insurance pricing.

The current research has considered the design of the four-level supply chain of perishable goods, including manufacturing factories, distribution centers, wholesalers, and retailers, in conditions of uncertainty in important parameters. The aim is to make strategic and tactical decisions, including the location, number, and size of distribution centers and wholesalers, stock levels in stocking centers, determining the flow of goods between facilities at different supply chain levels, and choosing the type of means of transporting goods between facilities. This is achieved through a three-objective mathematical model. The goals include minimizing the expected total cost in the supply chain, achieving the shortest travel time of goods in the chain, and at the same time minimizing the amount of deviation from customer demand. The presented model tries to pay attention to environmental uncertainty and consider different operational scenarios, as well as the possible approach in important parameters. This takes into account the product life cycle, the different rate of spoilage of the goods in different storage facilities, the different capacity of the facilities in different scenarios, and considering different methods of product transportation with different rates of product spoilage. All of this aims to cover the lack of previous research in the field of perishable goods supply chain design. Considering the multi-objective nature of the model and the need to create flexibility in decision-making for decision-makers, this research uses Normal Boundary Intersection (NBI), which allows decision-makers to choose the most optimal solution according to the importance of different goals. GAMS 24 software and MILP solver were used to solve the mathematical model.

Today, many fields of research and business deal with the thoughts, images and mental patterns of their users and audiences. Knowing and being aware of the way of thinking of people, customers and audiences of a collection has a great impact on making appropriate decisions by the managers of that collection, in order to advance the goals and solve various problems. One of the important requirements of managers in any collection is to have sufficient knowledge of how they think about the collection and its performance. This helps them guide the group properly and make appropriate decisions in the way of advancing its goals. Therefore, collecting data from customers should be done with measured and accurate methods to achieve high quality information and obtain valid results. This research, by raising the question of "how can you use the analysis of people's feelings to find out how they think about a certain issue?", tries to provide a new approach on the platform of the Twitter social network to obtain reliable and high-quality information from people in relation to a certain topic. The purpose of the current research is to provide a new approach to collect data from people, in order to measure their perception about a specific issue. It will also examine the impact of the presented method on the speed, quality and cost of collecting data from people using a case study approach.

In this research, their comments on the social network Twitter were used to check the satisfaction of users of three internet taxi applications in Iran, namely Snap, Tapsi, and Carpino. The data collection approach used in this research was descriptive, with results used by users, managers, and researchers. The research population consisted of all users of the three applications and members of the Twitter social network who had published their thoughts about these applications. The research collected and analyzed a total of 682 relevant tweets cross-sectionally during the summer of 1400 using the Twitter application interface and related hashtags. The analysis steps included data collection, pre-processing of tweets - including linguistic, sentiment, and thematic analysis, followed by sentiment analysis.Discussion and

The comparison of the analysis results from this research to the face-to-face interview method showed that the participation of respondents was challenging, and the traditional field method came with its own disadvantages. However, this method is able to overcome those issues and provide reliable results at a lower cost and time. Moreover, the results obtained from analyzing the data collected were similar to the results obtained through the face-to-face interview method, which speaks to the accuracy and quality of the data. Overall, the proposed method can provide valuable insights into people's thoughts and opinions on a particular topic, at a lower cost and time.

The proposed method can be widely applicable to various fields, including business, research, and even everyday life. Managers, researchers, and business owners can use it to collect data and insights from their target audience, which can be analyzed to make informed decisions. Further development of the method can lead to even more useful and accurate reports, helping individuals and businesses stay ahead of their competitors. All students and researchers dealing with data collection can use this method to conduct their studies more effectively. Additionally, market research companies can use it to gain valuable feedback from consumers, informing their next steps in providing products and services.Overall, the applications of this research are far-reaching, and it provides a valuable resource for individuals and organizations looking to better understand their audience.Keywords: Sentiment analysis, text mining, how people think, Twitter, Internet taxi.

The purpose of this research is to design and plan the production and distribution supply chain in an integrated manner.

By using the bi-level programing approach, it is possible to maintain the integrity of decision-making while managing the conflict between the goals and interests of the decision-makers. In the proposed model, supply chain design decisions are considered at the leader level and production and distribution planning decisions are jointly considered at the follower level. At the leader level, the three goals of minimizing the costs of the entire supply chain, maximizing reliability and minimizing adverse environmental effects, and at the follower level, minimizing production costs and minimizing distribution costs are pursued. Therefore, the proposed bi-level programing model is multi-objective and multi-follower. To calculate the credibility of SCOR12 model parameters, hierarchical data analysis technique and fault tree analysis were used. The carbon dioxide emission factor was used to measure the adverse environmental effects. Finally, meta-heuristic technique has been used to solve the proposed model.

The findings indicate that due to the high accuracy of the MOEA/D algorithm, the findings of this algorithm can be trusted for the supply chain problem.

This research is considered a new approach in terms of leveling the problem and choosing the problem of leader and follower. If the decisions related to the design and planning of the supply chain are made with the cooperation of the supply chain links and in an integrated manner, the probability of achieving more favorable results increases