meysam shahbazi

This study investigates the factors affecting the commercialization of products in medical biotechnology accelerators, it also demonstrates the priority of these factors. The research is applied and descriptive survey. It has been done in two phases, qualitative and quantitative. Based on the general purpose of this study, to provide a framework for the commercialization of products in medical biotechnology accelerators, we interviewed with informed experts. 62 sub-codes were obtained as effective factors in the commercialization of products in medical biotechnology accelerators, which were classified into 5 main dimensions including individual, organizational, industry and competition, institutional and product. These dimensions are presented as a framework. In the quantitative phase, the main dimensions as well as the sub-themes were ranked in the main dimension. Finally, the priority of the main dimensions was as follows: 1. Individual (personality, behavioral and mental characteristics of the mentor and manager) 2. Industry and competition (interaction with pharmaceutical companies as customers, investors, etc.) 3. Organizational (experience of startup teams, codified strategy, R&D, etc.) 4. Product (value added and high-tech) 5. Institutional (government is pragmatic to the country's slogans, interaction with government managers, etc.).

This study aimed to design a comprehensive model for intelligent organizational knowledge mapping with a hyper-hybrid approach based on meta-ethnography. In this article, various sources and theories about the knowledge map have been examined and analyzed using the meta-combined and meta-ethnography methods. Using the results of the work, a comprehensive model of knowledge map has been developed. Based on the qualitative approach in this research, the meta-hybrid method based on meta-ethnography has been used. The tool used has been articles related to  mapping knowledge for smart organizations. A total of 34 pieces of research were found with the keywords related to designing and developing the knowledge map in the title field. Finally, after 4 stages of refinement, out of 34 studies, 8 were excluded and 26 studies were selected for data analysis. In this article, by examining and analyzing various patterns of knowledge map development, finally, an 11-step model was developed. The proposed model of eleven basic steps, respectively, includes: determining the knowledge objectives of these centers by developing knowledge maps and identifying users of knowledge maps, identifying definitions and terminology of knowledge centers, identifying the basic processes of these centers, and drawing and analyzing its process maps, Knowledge extraction from the activities in the processes of smart organizations according to the process map, creating a profile for the extracted knowledge, creating knowledge connections and relationships and integrating these connections into an illustrated interface, and finally validating the developed knowledge map.

Anti-fragility allows the organisation to place itself in a position to benefit from the unknown. The purpose of this study is to identify and evaluate the indicators affecting the Anti-fragility of the insurance industry supply chain and to determine the causal relationships between these indicators. This study has been conducted through an exploratory mixed-method. The Delphi method and the Gray-DEMATEL technique have been utilized in this study. Insurance companies must implement indices, methods, and mechanisms to promote the anti-fragility of their supply chain, though simultaneous attention to all indices is impossible and uneconomic. Therefore, deciding which indices have priority in investment can be accomplished using the research findings and determining the significance of each index, as well as the causal relationships between them. According to the results of the Delphi method, 49 indices for anti-fragility of the supply chain insurance industry, hereunder the eight main processes, have been extracted. Study results also revealed that in order to lead the supply chain insurance industry towards anti-fragility, the "knowledge and information management" and "risk management" processes should at first be considered. According to expert opinions, the "management information system" and "knowledge management implementation" indices have been selected as the most causal indices of anti-fragility of the knowledge and information management process, while the indices of "risk strategy development" and "using the risk database" have been chosen as the most causal indices of risk management anti-fragility.

Blood as a vital element of the health system has an important role in this system, because any blood deficient results in death. Blood supply chain management aims to bridge the gap between blood donors and consumers. This results in no blood deficient, and minimization of, the corruption of blood products and lower costs. Humanitarian supply chain includes planning, managing activities related to materials, information, and finances when providing relief to affected people

The purpose of this study is to optimize the sustainable humanitarian supply chain of blood products in Tehran. In this study, a five-echelon blood supply chain network is presented that includes donors, mobile, fixed and regional blood collection centers, and hospitals. This study proposed a multi objective mixed integer linear programming optimization model considering economic, environment and social objectives. Due to the uncertain and random nature of blood supply and demand and cost parameters, the model develops into a robust optimization model. The ε-constraint method is used to transform the multi-objective problem into a single-objective mode. The model is coded in GAMS and solved by CPLEX solver.

The model outputs include allocate blood donor to mobile and fixed blood collection centers, allocate fixed and mobile blood centers to regional blood centers, and allocate regional blood centers to hospitals. The application of the proposed model is investigated in a case problem in Tehran where real data is utilized to design a network for emergency supply of blood during potential disasters. The results indicate robust model is more efficient in controlling demand, supply and costs uncertainties. At ω = 100 the supply chain cost reaches to $51746 and unmet blood demand is zero under any earthquake scenario and, the supply chain is robust in all scenarios.

The proposed robust optimization model is useful for managers, as well as researchers, who have studied location–allocation of facilities in the blood supply chain network in post-disaster periods. To provide some managerial perspectives for the aforementioned problem, a sensitivity analysis has been performed. Important practical implications were drawn from the case study results. We showed how solution robustness (supply chain cost) can be balanced against model robustness (unmet demand). As the value of risk aversion weight, increases, the total cost indicating solution robustness increases, while the blood unmet demand indicating model robustness decreases. Finally results analysis, concluding remarks and directions for further research are presented.

Due to the explosion of data in recent years and the increase in its commercial application, big data and its analytics have attracted the attention of governments, academics, and practitioners, especially that of the startups. At the same time, regarding the recent nature of the phenomenon, entrepreneurship literature lacks a thorough understanding of the contextual challenges experienced by big data startups. Therefore, this study aims to contribute to the literature of big data entrepreneurship by identifying the environmental challenges of big data startups and exploring the priority of these challenges in the context of Iran, a developing IT market country. For this purpose, our research method is comprised of two phases: identifying the challenges of big data startups' using in-depth interviews and questionnaire, and prioritizing such challenges based on experts' assessments using a fuzzy analytic hierarchy process. Our findings revealed that lack of particular laws and policies for big data, lack of transparency, data mismanagement, financial challenges, technological and educational weaknesses, unawareness of big data advantages, and lack of willingness for utilizing it are the most significant challenges of big data startups in a developing context. The paper concludes with some policy and managerial recommendations for creating a supportive environment for startups.

This research aims to analyse key stakeholders in Tehran air pollution control project during its life cycle.

Since the research is qualitative, after librarian research and gathering research literature, the actors of Tehran air pollution control project were recognized and the outputs analyzed using Delphi method. Key actors were determined after Delphi method application and analyzed using power-interest matrix during life cycle.

in the present study, 12 key actors identified and selected from 23 actors as follows: “Ministry of Industry, Mine and Trade”, “Environmental Protection Agency”, “Islamic Republic of Iran Broadcasting Organization”, “National Standard Organization of Iran”, “Tehran Air Quality Control Company”, “Deputy of Transport and Traffic of Tehran Municipality”, “Insurance organizations”, “Oil Ministry”, “traffic police”, “Automakers”, “Ministry of Health and Medical Education”, “Environmental Protection and Sustainable Development Organization of Tehran Municipality”.

in the present study, key actors changed in each stage of projects life cycle while in almost all similar projects this fundamental change has been ignored in the project run time. also after reviwing formal documents, laws and strategies that have been written for solving air pollution crisis it was found that “Insurance organizations” have been ignored as a key actor in solving this crisis.

To connect and share resources between industrial units with unused resources on the one hand, and production systems in need on the other, a concept called “sharing economy " can be used. If sharing economy is sufficiently explained, and its theoretical aspects strengthened, more actions perform to make this concept practical. Therefore, in the present study, after studying the literature and previous studies, the components of sharing economy are identified and generalized to production systems. Utilizing a sharing economy will have a significant impact on increasing the productivity of production systems through resources sharing.  

In the first phase of this research, the library study method used to collect data and information. Content analysis method used to analyze the data and identify the dimensions and components of the sharing economy. Then, in the second phase of the research, the interview process and expert opinions were used to determine the dimensions, elements, and examples of sharing economy in production systems. After extracting questions from the concepts extracted from the first phase of the research and after conducting the interview process and summarizing it, the components and dimensions of the sharing economy in production systems validated and their examples identified.  

In this study, the components of sharing economy in production systems identified as follows: resource sharing, shared parties, forms of sharing, information and communication exchange system, ownership and access conditions, surplus capacity, exchange benefits for sharing parties and others, and social issues in sharing. These elements categorized into four dimensions: sharing mechanism, sharing requirements and conditions, sharing purpose, and sharing consequences.  

Considering the wide range and levels of coverage of the concept of sharing economy and its various operational areas, summarizing the definitions and extracting the components is highly complicated. It also seems hard to reach experts who work in the field of production systems and sharing economy or those who are familiar with its concepts. The following study subjects suggested to develop sharing economy in production systems: i) counting and examining the challenges facing the sharing economy, such as legal issues, building trust, ethical issues, insurance and taxes, and providing solutions to solve it; ii) the role of resource sharing and sharing economy in lean, agile, sustainable, green, and resilience of production systems; iii) the role of social issues regarding the sharing economy in production systems or designing a socio-economic model for it; and iv) determining the type of relationship between the dimensions and components of the sharing economy and how they affect and intensify each other with the help of methods such as structural equation modelling.  

The use of sharing economy leads to better use of resources and reduced cost of using it, reduced product cost, increased competitiveness and ultimately improved efficiency and productivity of the production system. For the supplier of resources, resource sharing in production systems leads to the prevention of stock death, earned profit and strengthened financial resources; and for the recipient of resources, it leads to reduced cost of access to resources and investment, and thus saved liquidity. In addition to the sharing parties, intermediaries and service providers, government and government agencies, the country's economic market, the working community, society, consumers and even scientific and research centres benefit.  

Resource sharing in production systems can affect or be affected by social issues. The attitude of rulers and politicians in governing society and passing laws, and overseeing their implementation can affect the concept of a sharing economy. Sharing helps in increasing social capital in the areas of education and public awareness, building trust, increasing interactions, and connecting production systems with social participation, and increasing the general level of community culture. Sharing strengthens a sense of social responsibility and can also be significant in the field of social justice and reducing social harm.  

If the concept of sharing economy explained sufficiently and its theoretical aspects strengthened, it can be operational in the next steps. In this regard, in this study, the dimensions and elements of the sharing economy in production systems identified. These dimensions and components can provide the basis for designing a practical and operational model for implementing a sharing economy in production systems, can reduce the gap between practice and theory, and can guide managers and business activists and those involved in the field.