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This paper aims to measure the sensitivity of the structure’s deformation numerical model (NM) related to the various types of the design parameters, which is a suitable method for parameter selection to increase the time of model-updating.

In this research, a variance-based sensitivity analysis (VBSA) approach is proposed to measure the sensitivity of NM of structures. In this way, the contribution of measurements of the structure (such as design parameter values and geometry) on the output of NM is studied using first-order and total-order sensitivity indices developed by Sobol’. In this way the generated data set of parameters by considering different distributions such as Gaussian or uniform distribution and different order as input along with, the resulted deformation variables of NM as output has been submitted to the Sobol’ indices estimation procedure. To the verification of VBSA results, a gradient-based sensitivity analysis (SA), which is developed as a global SA method has been developed to measure the global sensitivity of NM then implemented over the NM’s results of a tunnel.

Regarding the estimated indices, it has been concluded that the derived deformation functions from the tunnel’s NM usually are non-additive. Also, some parameters have been determined as most effective on the deformation functions, which can be selected for model-updating to avoid a time-consuming process, so those may better to be considered in the group of updating parameters. In this procedure for SA of the model, also some interactions between the selected parameters with other parameters, which are beneficial to be considered in the model-updating procedure, have been detected. In this study, some parameters approximately (27 per cent of the total) with no effect over the all objective functions have been determined to be excluded from the parameter candidates for model-updating. Also, the resulted indices of implemented VBSA were approved during validation by the gradient-based indices.

The introduced method has been implemented for a circular lined tunnel’s NM, which has been created by Fast Lagrangian Analysis of Continua software.

This paper plans to apply a statistical method, which is global on the results of the NM of a soil structure by a complex system for parameter selection to avoid the time-consuming model-updating process.

The study aims to identify the dynamic complexities and development points of the entrepreneurial ecosystem (EE) in the agricultural sector of Iran to improve production factors' productivity, including arable land, water resources and human capital.

First, the EE of the agricultural sector in Iran was designed following Isenberg's framework. Then, the main variables and interrelationships of the variables in each context of the ecosystem, called subsystems, were formulated using the system dynamics (SD) approach. Next, the model was simulated and validated. Afterward, different policy options were identified, embedded into the model structure and simulated. Finally, the best policy group was selected.

According to Isenberg's EE model, three groups of policies were identified and evaluated, including “entrepreneurship development financing and investment policy,” “agricultural ecosystem's supportive services development policy” and “production factors productivity development policy.” According to the simulation results, the best combination of the solution strategies was recognized. The presented SD-EE model has a generic nature in the agricultural sector and could be modified to be applied in different regions for policy-making purposes.

The main contribution of the study is twofold. First, Isenberg's EE framework is applied to structure the main subsystems and interrelationships of the subsystems in the agricultural sector that has previously received limited attention. Second, the research is the first to operationalize the basic theory of Isenberg's EE in practice applying a robust systemic modeling methodology like SD.

This paper aims to identify the success factors of communities of practice (CoPs) that will improve knowledge creation (KC) in educational organizations. Today, educational organizations are faced with constant changes in their environment. These changes have forced organizations to view their human capital as knowledge capital to survive and thrive. CoPs can be one of the most important factors to achieve these goals, and it should be considered by educational organizations.

The present paper tries to identify the success factors of CoPs and examine the impact of the success factors of CoPs in KC. The research method is descriptive. The population of the research consists of students of Payame Noor University as a case study. A questionnaire based on experts’ opinion was designed and more than 35 articles were reviewed. Factor analysis was used to categorize and examine the correlation between success factors of CoPs and KC and, finally, the regression equation based on correlation’ result was formed.

The results showed six success factors: “organization”, “optimizing interactions”, “infrastructure”, “supporting tools”, “strategy and goals” and “organizational support”. These factors reflect the effective factors in educational organizations attempting to progress in KC should focus. Among these success factors, “strategy and goals” and “organization” have the highest effect on CoPs in this case.

The paper discusses how the success factors of CoPs improve KC in universities as educational organization and how an organization will benefit from these factors. It can help the development of guidelines for educational organizations to increase their community knowledge information. They can also apply the findings of this research to promote the scientific level of their related organizations through reinforcement of CoP practices. In this paper, the authors faced some problems to collect expert’s opinion and also gather the questionnaires.

This paper may have great value to researchers, as well as practitioners involved in knowledge-creation programs in organizations about CoPs and the knowledge-creation field. This research can establish guidelines for Payame Noor University Tehran-North as a case study using the right policies and make changes to the traditional structures of the university, improve and develop the knowledge-creation process and the formation of CoPs. This study can guide managers and, especially, the university managers to provide suitable base to shape COPs, help them emerge and enhance them effectively.

Ensuring energy security and controlling the share of energy in climate change are the two main challenges of the energy sector in the path of a sustainable future. This study has designed the dynamic model of Iran’s electrical energy supply system based on the water-food-energy-climate change nexus in order to identify sustainable policies for the supply of electrical energy resources and adaptation to Iran’s climate change process.

For this purpose, first, the system dynamics model was designed with the participation of policymakers and the supply and demand data of water-food-energy resources and the trend of climate change and economic growth in Iran. After validation, the model is simulated in the 30-year horizon (2020–2050), and according to the results of the Monte Carlo sensitivity analysis, Iran’s electric energy supply policies are based on four strategies, including (1) Electric energy supply based on electric energy supply management from non-renewable sources, (2) Development of electrical energy supply based on management of energy supply from renewable sources, (3) Electrical energy supply based on electrical energy demand management and (4) Electrical energy supply based on adaptation to climate changes. By identifying and applying the policies of each strategy separately, the model was tested and the results were compared.

Based on the implementation of the combination of selected policies in the model, the following policies have been proposed: 16% development of nuclear power plants, 18% reduction in the ratio of production of gas power plants to the total production of non-renewable power plants, and an increase in the production of combined cycle power plants through conversion of gas to combined cycle, energy aggregation and development of heat recovery systems in industrial units by 32%, a decrease of 5% Energy transmission and distribution losses, per capita reduction of energy consumption from 0.926 to the global average of 0.182 (MW) per year, management of water demand in the food sector by increasing irrigation efficiency to about 85%, a 27% increase in the area of land under the irrigation network, and reducing losses Food amounting to the global average of 0.9 m tons per year.

The proposed model is an application of system dynamics in the field of policymaking to ensure the security of electrical energy resources, taking into account the water-food-energy-climate changes nexus. The model is a valuable tool for policymakers in planning the sustainable management of resources in the path of adapting to climate change.

This research intends to develop a model for predicting employees at a high-risk attrition and identify the most important factors affecting them.

In this study, using the triangulation technique of a mixed research method, the employee attrition problem is investigated by identifying its affecting factors. For that matter, data related to the human resources department of a pharmaceutical company in Iran are used. And to achieve the intended goal, advanced data mining algorithms and interviews with human resource managers are applied.

A model for predicting employees at a high-risk attrition is presented based on the gradient boosting machine algorithm with 89% accuracy. The use of the mixed research approach shows that qualitative and quantitative methods can be more effective in identifying the factors affecting employee churn or loss of staff. The results also contain a new situation arising out of the COVID-19 pandemic and remote working scenarios having impact on employee attrition. Finally, human resource policies are presented based on variables related to each of the identified factors.

The novel contributions of this study include real data related to a leading pharmaceutical company as well as a combination of two quantitative and qualitative methods. The hybrid approach can identify the reasons for attrition and, consequently, retention policies to benefit from the advantage of both approaches. Data mining can be useful to identify the factors, which are usually not mentioned in termination interviews, such as direct managers. On the other hand, the results obtained from termination interviews can also include features that the authors cannot identify through data mining, which are specifically related to the characteristics of the pharmaceutical industry such as building a more professional career path. From a practical perspective, since this company specializes in pharmaceutical marketing in a new way and is primarily comprised graduates, it is important to note that the churn of specialized people disperses organizational and technological know-how. On the other hand, the pharmacist community in Iran is small, and their attrition might adversely affect not only the reputation of an organization but the employer's brand as well. So, this research would help other similar firms in retaining their valuable human capital.

Open innovation has attracted the attention of experts and business entities for the sustainable survivability of firms, especially in the post-COVID-19 era. The food and beverage industry has been facing sustainable survivability problems. It is important to identify and evaluate the factors of open innovation from the perspectives of the food and beverage industry. This study serves that purpose by identifying and evaluating the factors of open innovation in the post-COVID-19 era with a special reference to Pakistan's economy.

The present study integrates the Fuzzy Delphi Method (FDM), Interpretive Structural Modelling (ISM) and Matrice d’ Impacts Croises Multiplication Applique a Classement (MICMAC) methods to analyze the factors involved in the adoption of open innovation in the food and beverage industry in Pakistan. Firstly, based on an extensive literature review of the most relevant studies, the factors affecting open innovation have been identified and finalized using FDM and experts' opinions. Secondly, the hierarchical framework has also been prepared by implementing the ISM approach. Thirdly, the MICMAC approach was employed to evaluate the factors to examine the driving and dependence powers of the factors of open innovation adoption.

The study identified 17 factors of open innovation adoption in Pakistan's food and beverage industry and 16 factors were finalized using FDM. The ISM-MICMAC matrix unveiled that awareness seminars and training, along with a lack of executive commitments, were strong factors with high driving power, but these factors proved to be weakly dependent powers regarding the other factors. Moreover, a lack of innovation strategy, R&D and non-supportive organizational culture exhibited low driving power but strong dependent power.

The findings of the study could help firms and business entities understand the driving and dependent factors involved in open innovation for the sustainable survivability of the food and beverage industry. The study provides strong reasons to believe that an open innovation strategy, along with stakeholder collaboration, the adoption of rules and regulations and managerial commitment, could stimulate open innovation. Moreover, governments should promote the business sector, especially the food and beverage industry, to facilitate the sector while also providing awareness seminars and training, creating environments conducive to reducing innovation costs.

Some previous studies have analyzed the factors involved in green innovation from the perspective of the manufacturing industry and environmental protection. The present study is a pioneer study to examine the factors involved in the adoption of open innovation in the food and beverage industry in Pakistan from the perspective of the post-COVID-19 era. For this purpose, the present study uses an integrated Fuzzy Delphi-ISM-MICMAC approach for the analysis.