نشریه مدیریت پویا و تحلیل کسب و کار
سال سوم شماره 1 (پیاپی 6، بهار 1403)

This study aims to develop a roadmap for the growth of industrial startups using the Blue Ocean Strategy approach. 

This research is applied in nature and descriptive-survey in method. Initially, information was gathered from literature reviews, field studies, and comparative analyses. Then, 19 experts and specialists in the field of industrial startups were interviewed, selected through purposive sampling, to extract the components and variables of this domain. The obtained indices were screened using the fuzzy Delphi method over three rounds. In the quantitative section, the statistical population included managers and stakeholders of industrial startups, with 60 individuals selected through simple random sampling and Cochran's formula. The final Delphi questionnaire was distributed among this sample. The validity of the questionnaires was confirmed by six startup experts with PhDs and at least five years of managerial experience in the field. Reliability was tested using Cronbach's alpha and composite reliability. Data analysis was conducted using structural equation modeling and PLS software. 

The findings indicated that the derived indices were strongly validated. These indices included 38 indicators across 11 components and 5 dimensions, each playing a significant role in the development of industrial startups. Key components such as innovation, creativity, and the use of information technology and the internet showed a positive and significant impact on startup development. 

The results demonstrated that developing a roadmap for the growth of industrial startups using the Blue Ocean Strategy approach can significantly aid the growth and development of these businesses. Additionally, using this approach can reduce market competition and create new opportunities for startups. These findings underscore the importance of precise policymaking and effective support from both the government and the private sector to provide the necessary infrastructure for startup success.

The objective of this study is to design scenarios that can examine brand switching behavior in customers within the sports equipment industry and identify and analyze the influencing factors. 

In this research, first, a literature review was conducted to identify the factors influencing brand switching behavior in customers. Then, a questionnaire was designed and distributed to examine the current status of these factors in the sports equipment industry in the country. Seventy experts from the sports equipment industry responded to this questionnaire. Subsequently, using the fuzzy cognitive map technique, the systemic relationship between the factors was identified, and forward and backward scenarios were developed to improve the structure. 

The results of the study indicate that among the 10 identified factors, the quality of products and services offered in sports equipment is the most important factor in implementing a systemic structure to prevent brand switching. The identified factors are: trust, competitive pricing, customer attitudes, customer satisfaction, reliability, quality, customer relationship management, word-of-mouth advertising, knowledge management systems, and government regulations. 

Focusing on the quality of products and services offered in the sports equipment industry can serve as an effective strategy to reduce brand switching among customers and provide a distinct competitive advantage for brands.

 This study investigates the impact of optimistic investor sentiment and stock liquidity on the stock returns of the Iranian stock exchange. The primary objective is to analyze how psychological and financial factors influence stock market performance.

The research utilizes monthly data on stock returns, investor sentiment, and liquidity from April 2013 to June 2022. Two methods, Dynamic Ordinary Least Squares (DOLS) and Robust Least Squares (RLS), were employed for data analysis. These methods allow researchers to precisely examine the longitudinal and robust relationships between the variables.

The results indicate that optimistic investor sentiment has a positive and significant effect on stock returns, while illiquidity has a negative and significant impact on stock returns. These findings underscore the importance of managing market-related information and news to channel investor sentiment positively.

The results of this study suggest that proper management of investor sentiment and increased liquidity can improve stock returns and stabilize financial markets. It is recommended that stock market regulators and economic policymakers utilize these findings to enhance stock market performance through appropriate policy-making and implementation. Additionally, directing liquidity from parallel markets to the stock market can help control inflation and reduce price volatility.

various related scenarios. The study aims to identify and assess different financial and investment policies that could enhance investment attraction in Iran's oil industry.

The study employs system dynamics and content analysis to develop the model. The statistical population for the content analysis includes upstream oil industry documents and related articles on investment. In the quantitative section, employees, managers, and experts in the oil industry were selected as the sample population. Data analysis was conducted using Vensim software, and nine different scenarios were developed to evaluate financial and investment policies.

The results revealed that intermediary financing, with an increase of 128.854 units, was identified as the most attractive policy. Debt-based financing, with an increase of 108.333 units, and financing based on underground reserves, with an increase of 69.580 units, followed. These findings highlight the importance of diversifying financial resources and utilizing innovative financial structures in Iran's oil industry.

The study concludes that a combination of different financial resources and the application of diversified financial policies can significantly contribute to attracting more investments and reducing financial risks in Iran's oil industry. These findings align with previous studies on financial and investment policies and provide valuable insights for policymakers in Iran's oil sector.

This study aimed to develop a comprehensive model of quality of work life based on organizational justice, organizational optimism, and organizational resilience among employees of the Education Departments in Tehran.

The research employed a mixed-method (qualitative-quantitative) approach. In the qualitative phase, semi-structured interviews were conducted with 15 experts in management and sociology, and the initial data were collected and analyzed. Following this, a questionnaire was designed based on the qualitative findings and distributed among 450 employees of the Education Departments in Tehran. Data analysis was conducted using Structural Equation Modeling (SEM) and the AMOS and SPSS software.

The findings revealed that all three variables—organizational justice, organizational optimism, and organizational resilience—are significantly related to the quality of work life. Organizational justice was identified as the strongest predictor of quality of work life, while organizational optimism and organizational resilience also had direct and indirect impacts on improving quality of work life. Additionally, organizational justice played a mediating role in the relationship between organizational optimism and resilience with the quality of work life.

The results of this study indicate that attention to organizational justice, organizational optimism, and organizational resilience can lead to an improvement in the quality of work life for employees in educational organizations. It is recommended that managers strengthen these variables to create a supportive and positive work environment, thereby enhancing employee satisfaction and commitment.

The objective of this study was to design a systemic relationship model for value enhancement from the perspective of sustainable development in construction project management.  

This research employs a mixed-method approach (qualitative and quantitative) conducted in three phases. In the first phase, a systematic review of scientific documents was performed using a meta-synthesis qualitative approach, leading to the identification of 9 major themes and 50 sub-themes. The thematic model was then localized into 48 sub-themes within 9 main themes using the fuzzy Delphi method. In the second phase, the localized model was converted into a questionnaire and distributed among experts. The collected data were analyzed using confirmatory factor analysis (CFA). In the third phase, the internal relationships between the themes were examined, and the final systemic relationship model was developed.  

The findings revealed that key themes such as value governance quality, continuous evaluation and feedback, communication quality, knowledge management, and coordination within the supply chain play a crucial role in enhancing sustainable value in construction projects. These factors contribute significantly to improving project performance in achieving sustainability goals.  

This study demonstrates that value enhancement from the perspective of sustainable development in construction project management requires attention to a set of critical factors that can improve project performance in sustainability. The proposed systemic relationship model can serve as an effective managerial tool for promoting sustainable value in construction projects.

The primary objective of this study was to analyze the effectiveness of an e-learning model as a tool for digital marketing aimed at attracting health tourists.

This study utilized a mixed-method exploratory approach, incorporating both qualitative and quantitative research methods. In the qualitative phase, in-depth interviews were conducted with 14 experts in management and digital marketing, selected through purposive and snowball sampling. The quantitative phase involved a survey of 400 employees from educational and medical centers in Tehran, with 233 valid responses analyzed using descriptive and inferential statistics. The final e-learning model was validated through confirmatory factor analysis.

The study identified eight critical dimensions and 47 components essential for an effective e-learning model in health tourism marketing. These dimensions include website optimization, content quality, social media engagement, video marketing, cultural and linguistic adaptation, customer relationship management, data analytics, and sustainable practices. The model was validated and shown to align well with the needs of the health tourism market.

The findings emphasize the crucial role of digital marketing in attracting health tourists. An effective e-learning model that incorporates the identified dimensions can significantly enhance the visibility and appeal of health tourism services. The study underscores the importance of adapting digital marketing strategies to technological advancements and consumer behavior changes. Future research should explore the integration of emerging technologies such as artificial intelligence and virtual reality in digital marketing for health tourism.

The purpose of this research is to present a social media marketing model in the fashion and apparel industry using grounded theory.

The nature of the present research is exploratory, employing qualitative data. The statistical population of the research includes experts and Ph.D. professors in marketing management at universities in Mazandaran Province. The researcher utilized the snowball sampling method to achieve theoretical saturation, which was attained after conducting interviews with 14 experts and scholars. Qualitative data were collected using structured interviews and analyzed through the qualitative grounded theory method.

Four axial codes were identified and reported for causal factors, including online electronic business developments, online electronic business experience, characteristics of online customers, and social media marketing capabilities. For contextual factors, four axial codes were identified and reported, including online digital culture, online digital thinking, online electronic transformation infrastructure, and online digital leadership. Additionally, four axial codes were identified and reported for intervening factors, including cultural factors, social media challenges, and social and legal factors. For the central phenomenon, three axial codes were identified and reported, including social media marketing tools, social media marketing advertising, and social media marketing performance. For strategies, five axial codes were identified and reported, including the use of influencer marketing, investment in social media marketing, digital knowledge orientation, digital culture creation, and logistics (support) strategies. Finally, customer attitudinal and behavioral outcomes, as well as organizational outcomes, were identified.

This research was initiated to develop a social media marketing model in the fashion and apparel industry using grounded theory. Through the qualitative method, the research progressed and reached a comprehensive model. At the end of the study, recommendations based on the researcher’s experiences are presented.

The objective of this study is to develop a comprehensive talent management model tailored to the unique needs and cultural context of the employees at Astan Quds Razavi.

A mixed-methods approach was employed, integrating qualitative and quantitative methodologies. The qualitative phase involved thematic analysis using Clarke's method, facilitated by MAXQDA software, and included interviews with 15 purposively selected experts from Astan Quds Razavi. The quantitative phase targeted 21,000 employees, with a sample size of 147 determined using G*Power software. Data were collected through surveys and analyzed using path analysis and variance-based structural equation modeling with SPSS and Smart PLS 3.

The study identified six key predictors for effective talent management: talent evaluation, talent engagement, talent identification, talent culture, professional development of talent, and the attraction and retention of top talent. Additionally, 30 specific indicators were identified that contribute to these predictors.

The proposed talent management model provides a robust and context-specific framework for managing talent within Astan Quds Razavi. The model addresses the unique needs of the organization and aligns with its cultural context, offering a valuable tool for improving organizational performance and employee satisfaction. This model has the potential to be adapted for use in similar organizations and contributes to the broader understanding of effective talent management strategies.

The objective of this study is to conduct a systematic review and meta-analysis of mutual fund performance evaluation studies, focusing on the historical evolution of performance assessment methods based on fund style.

This research is divided into two main sections. The first section involves a quantitative analysis of prominent ISI-listed studies on mutual fund performance from 1900 to 2023, using bibliometric software such as HistCite™ and VOSviewer. This bibliometric analysis identifies and categorizes key studies based on citation counts. The second section reviews the evolution of mutual fund performance measurement criteria and various approaches used in the literature, critically analyzing the strengths and limitations of these methods.

The bibliometric analysis revealed significant trends in mutual fund performance evaluation, with a clear evolution in methodologies over time. The study identified two primary approaches: asset-based analysis and return-based analysis. Key findings indicate that while both approaches have their merits, asset-based analysis offers a more accurate assessment of managerial skill by considering the composition of the fund’s holdings. Additionally, the analysis highlighted the most cited studies and influential journals in the field, providing a comprehensive overview of the research landscape.

The study concludes that mutual fund performance evaluation has significantly evolved, with increasing complexity and sophistication in methodologies. The findings underscore the importance of selecting appropriate performance measurement criteria based on the specific characteristics of the mutual fund.

This research aims to design a model for the integration of physical, informational, and financial processes in the supply chain to achieve resilience in the automotive industry. 

The approach used in this research is modeling, initially utilizing Fuzzy DEMATEL to identify influencing and influenced factors. Since DEMATEL typically does not describe the nature and levels of interactions, the study further employs an Interpretive Structural Modeling (ISM) approach using MICMAC software to understand causal relationships and categorize influential factors into five levels. Data collection was based on a questionnaire and the Delphi method. A questionnaire grounded in a hybrid Fuzzy DEMATEL-ISM approach was designed and distributed to 20 supply chain management experts in the industry. After reaching a consensus, these factors were analyzed with the chosen approaches and classified into five levels, categorized into four types: autonomous, independent, dependent, and linkage.  

The findings indicate that eight categories of factors (including integration of measurement, resource management, communication integration, organizational; socio-economic sub-component; human and technical sub-component; managerial components, customers, planning, information and product quality; financial, process, and environmental components; technology planning integration and flexibility) have the highest impact on achieving resilience in the automotive industry supply chain. 

The most influential factor in the resilient supply chain of the automotive industry is the integration of measurement, which is connected with all other sub-components. In fact, resilience measurement impacts the resource management sub-component and the managerial sub-component.

The aim of the present study is to model the effects of volatility and uncertainty in psychological errors on the collective decision-making of investors in the Iranian capital market.

To test the hypotheses, a sample consisting of 204 companies from those listed on the Tehran Stock Exchange was selected using the systematic elimination method over a 5-year period from 2018 to 2022. The research data were collected on a monthly basis with a panel data approach and were analyzed using three multiple linear regression models. These models were calculated using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) and Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) methods, with the help of Excel 2016 and Eviews13 software for statistical analysis.

According to the results of the statistical tests of the research hypotheses, the volatility of the psychological error index and investors' trading behavior has a significant and negative impact on abnormal stock returns. Additionally, the volatility of the psychological error index has a significant and positive impact on the volume and frequency of abnormal stock trades. However, the volatility of investors' trading behavior has a significant and negative impact on the volume and frequency of abnormal stock trades.

The results of the analysis of the EGARCH and TGARCH conditional heteroskedasticity models indicate asymmetry in the influence of collective investment decisions from past data and past volatility, which is consistent with the Prospect Theory that claims positive and negative returns have different effects on investor utility.

 The objective of this study was to develop a model for the quality of work life (QWL) of nurses using a grounded theory approach.

 The study employed a qualitative research method with a grounded theory design. The research sample included 24 nurses from treatment-educational hospitals in Mashhad, selected using snowball sampling until data saturation. Data were collected through in-depth, unstructured interviews and analyzed using open, axial, and selective coding.

 The study identified key causal conditions influencing QWL, including the provision of welfare services, participation, career advancement, improvement of environmental conditions, fostering a favorable culture, and organizational justice. Strategic factors such as fair treatment, efficient incentive systems, utilization of competencies, and valuing were also significant. The outcomes of improved QWL were categorized into job satisfaction, productivity, multipurpose use of space, and organizational commitment.

 The proposed model suggests that improving both causal and strategic factors can enhance the quality of work life for nurses, leading to greater job satisfaction, increased productivity, and stronger organizational commitment.

This study aims to identify the factors influencing variety-seeking behavior in customers of online retail markets.  This is a qualitative and applied study. Data were collected through semi-structured interviews with 12 experts and experienced individuals in the field of online businesses and digital retail. Purposeful sampling was used to select participants, and the data were analyzed using grounded theory with open, axial, and selective coding. Maxqda software was employed for data analysis.  The study revealed that factors such as the weaknesses of online businesses, marketing culture, competitor appeal, purchase motivation, and the desire to experience new products directly influence customers' variety-seeking behavior. Pricing strategies, online marketing, and customer loyalty were also found to play a crucial role in either reducing or increasing this behavior.  Variety-seeking behavior in online retail markets is a complex phenomenon influenced by various factors. By improving service quality, creating a unique shopping experience, and utilizing targeted marketing strategies, online businesses can enhance customer loyalty and benefit from reduced variety-seeking behavior.

The aim of this study is to accurately estimate the cost of capital and manage risk in energy projects based on public-private partnerships (PPP) in emerging markets.
 
To explore the mechanism of determining capital costs and evaluating capital budgeting, Dynamic Conditional Correlation (DCC) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were employed. Additionally, the pure-play method was used as a tool for measuring systematic risk in energy-related projects in emerging markets.
 
The results showed that the DCC-GARCH model is capable of more accurately predicting volatility and capital costs in emerging markets. Moreover, the use of the pure-play method for calculating systematic risk led to reduced volatility and improved capital cost estimation. The findings also indicated that market volatility and risks associated with economic and financial policies in emerging markets have a significant impact on capital costs.
 
This study demonstrates that the application of advanced financial models such as DCC-GARCH and the pure-play method can lead to more accurate capital cost estimations in PPP projects and provide a better understanding of risks in emerging markets. These models offer valuable tools for risk management in the dynamic economic conditions of emerging markets.

 This article aims to present and estimate a human development model in Iran, with an emphasis on green finance and the FinTech index. Green finance is recognized as a key strategy in ensuring economic and environmental sustainability, while FinTech can facilitate access to financial resources and improve human development indicators. The primary objective of this study is to examine the impact of these two factors on human development in the period from 1996 to 2022.

 This study is applied and descriptive-analytical, utilizing statistical methods and modeling for data analysis. To measure the green finance index, five economic and environmental variables were employed. The Principal Component Analysis (PCA) method was used to extract the green finance index. Subsequently, the Markov regime-switching approach was applied to estimate the model and analyze the impact of green finance and FinTech on human development.

 The results from the Markov model estimation indicate that both green finance and FinTech have a positive and significant impact on human development. During periods of economic growth, employment, education, and capital also contribute to enhancing human development. The coefficients of green finance and FinTech remained positive in both recession and growth regimes, demonstrating their consistent influence on human development across varying economic conditions.

 This study concludes that green finance and FinTech can effectively promote human development in Iran. Utilizing green financial tools and modern financial technologies, especially during economic downturns, plays a crucial role in improving human development indicators and fostering sustainable economic growth.

 Work engagement has an effective role in improving other job and organizational functions. As a result, this research was conducted with the aim of providing work engagement model based on the blue ocean strategy and organizational resilience with the mediation of job self-efficacy in non-government school managers.

 This study in terms of purpose was applied and in terms of implementation method was cross-sectional from type of correlation. The present research population was non-government school managers of Tehran city in the academic years of 2023-24, which 400 people of them were selected as a sample by purposive sampling method. To collect data were used from questionnaires of work engagement (Salanova & Schaufeli, 2008), blue ocean strategy (Rajabi Farjad et al., 2019), organizational resilience (Prayag et al., 2018) and job self-efficacy (Riggs & Knight, 1994). For data analysis were used from methods of Pearson correlation coefficients and path analysis in SPSS-25 and Amos-25 software.

 The results indicated that the work engagement model based on the blue ocean strategy and organizational resilience with the mediation of job self-efficacy in non-government school managers had a good fit. Also, the variables of blue ocean strategy and organizational resilience on job self-efficacy and work engagement of non-government school managers and the variable of job self-efficacy on work engagement of them had a direct and significant effect (P<0.001). In addition, each of the variables of blue ocean strategy and organizational resilience with the mediation of job self-efficacy on work engagement of non-government school managers had an indirect and significant effect (P<0.001).

 According to the results of this study, the officials and planners of the educational system can improve the work engagement of non-government school principals by increasing their blue ocean strategy, organizational resilience and job self-efficacy.

 The aim of this research is to examine and test the model for enhancing administrative transparency in interaction with e-government development programs in the Social Security Organization.

 This research is applied in terms of purpose and descriptive-survey in terms of data collection method. Data were collected using a standardized researcher-made questionnaire. The statistical population in the quantitative section includes all employees of the Social Security Organization. The sampling method for the present study, considering the target population, is stratified sampling. Approximately 65,000 individuals are in the population, and based on Cochran's formula, a sample of 385 individuals was selected. Subsequently, the composite reliability and the reliability of each research component were calculated, with the results indicating the reliability of the measurement tool. Additionally, the divergent and convergent validity of the questionnaire suggests the validity of the instrument. For data analysis, structural equation modeling was employed using the Smart PLS4 software.

 The results indicate that e-government has a significant impact on the growth and development of the Social Security Organization. Administrative structure and processes significantly influence managerial oversight, public oversight, and public accountability. Environmental factors also significantly impact managerial oversight, public oversight, and public accountability. Furthermore, managerial factors have a significant effect on managerial oversight, public oversight, and public accountability. Organizational culture and atmosphere significantly affect managerial oversight, public oversight, and public accountability. Finally, managerial oversight, public oversight, and public accountability have a significant impact on e-government.

 The model for enhancing administrative transparency in interaction with e-government development programs in the Social Security Organization can be proposed as a comprehensive and practical package for all managers of executive agencies in general and for the Social Security Organization in particular.