حسین دیده خانی

The present study aimed to identify the organizational factors affecting the growth and success of academic spin-offs. The research was conducted based on a mixed method design and the study population consisted of experts of incubation centers and spin-offs. The participants were selected using purposive sampling in the qualitative part and random stratified sampling in the quantitative part. Data coding was employed to analyze qualitative data and exploratory and confirmatory factor analysis were used to analyze quantitative data. The qualitative results indicated that 26 organizational factors affected the growth and success of academic spin-offs. The quantitative results showed that “business planning to convert ideas into products” and “applicability of academic research” predict the highest and the lowest variance of the dependent variable, respectively.

This study aims to identify the factors affecting government support for Iranian automotive industry. Initially, based on the research literature, a set of factors was identified and designed as an analytic network process model. To investigate the analytic network model using the Delphi method, the opinion of 25 experts associated with government support for the automotive industry was used to answer the designed questionnaire for pairwise comparison. These experts were chosen through snowball sampling. The research result demonstrated that in experts’ opinion, various types of government support are needed to desirably expand this industry. According to the result of this study, a list of priorities has been provided below: 1) Specific support; 2) management information support and advisors; 3) financial support; and finally, 4) legal support. With respect to the result of binary comparison, the sub-criteria of reduced tax exemption for automakers, economic incentives, joint venture and provision of global marketing services have had the greatest weight respectively in the sub-criteria of financial support, specific support, legal support and management information support and advisors.

The current study aims to identify and rank the potential field of investment in the advanced technologies in Golestan Province. This is an applied and developmental research and the method for data gathering is descriptive and survey. The target society (decision-making team) includes all the industry elites of Golestan Province including ‘Ministry of Industry, Mine and Trade’, ‘The Industrial Towns Company’, ‘Standard Organization’, ‘Department of Environment’, and the deputy governors of Golestan Province. They were 15 people in total. A questionnaire was used to collect the data of the research. In order to analyze the data of descriptive statistics frequency distribution tables, frequency percentage, mean and charts were used. Also, in order to answer the research questions Analytical Hierarchy Process (AHP) and the different types of software like Excel, SPSS, and Expert Choice were used. The findings imply that “Biology-related elements” and “Nano-related industries” stand in the first and second ranks of investment in advanced technologies of Golestan Province respectively.

INTRODUCTION: Relief organizations, especially the Red Crescent, lack any specific entrepreneurial strategy and program for production, identification, and distribution of relief supplies. These organizations mainly focus on the preparation and distribution of supplies in times of crisis. In this regard, the present study aimed to design an entrepreneurial supply chain model with an emphasis on technology in 2020 in Iran. METHODS: The present study was conducted based on a qualitative and quantitative design. In the first phase, some indicators were obtained by observing the current situation and interviewing 30 experts. Following that, the final model was achieved by considering all indicators and categorizing the topics. In the Delphi process, experts' interviews and theoretical consensus suggested some hypotheses. In other words, in the second phase, structural equation modeling was used to finalize the model. In the next stage, the final questionnaire was provided to 186 Red Crescent employees. FINDINGS: After the analysis and extraction of the criteria from the interviews, components of the model were retrieved, and two questionnaires were designed. The first questionnaire was about supply chain management encompassing four main components of customer integrity, supplier integrity, internal integrity, and innovative orientation. The second questionnaire was related to technology, including seven components: personal characteristics, attitudinal factors, educational factors, technical factors, economic factors, environmental factors, as well as human and managerial factors. Considering the KMO value (˃0.7) and the significant value of the Bartlett Sphericity test, it can be concluded that the data are suitable for factor analysis. The model fit values all exceeded 0.9, indicating that the model has a “good fit. The path coefficients were significant for seven relationships at the level of 0.05. CONCLUSION: As evidenced by the obtained results, the supply chain in disasters requires experts' comprehensive approach and innovative perspectives. The tendency of countries to take innovative measures in disasters requires macro-policies at the national and regional levels. Therefore, all dimensions and aspects of the entrepreneurial supply chain in disasters must be considered in order to attain the final goal which is effective and efficient disaster management.