حسین دیده خانی

This article was conducted with the purpose of identifying the criteria of technological businesses based on cloud computing in small and medium-sized companies using the fuzzy Delphi method. The main concern of this research is to identify the criteria of technological businesses based on cloud computing in small and medium-sized businesses using the fuzzy Delphi method. It depends on mixed research (qualitative-quantitative) and collecting information through the background of previous researches and experts' opinions and the method of gathering information through questionnaires and interviews. Delphi's opinion of experts was taken. In the current research, many factors led to the formation of technological businesses; therefore, it should not be considered a simple phenomenon, and due to the intensity of electronic commerce in the world, the multi-dimensional concept of this phenomenon should be looked at more carefully.

Investor expectations future economic processes are among the crucial factors affecting their decisions. The expectations seem to play a specific role since they are unsupervised variables capable of forming observable economic phenomena. Psychological factors influence investor expectations and corporate market value. Investor sentiments was modelled with an emphasis on psychological factors based on the Grounded Theory (GT). This applied and mixed-methods at its first and second stages. The statistical population comprised 13 experts, senior managers of investment companies, and university professors. The participants were selected through purposive and snowball sampling and the process was continued until theoretical saturation. The data were collected via semi-structured interviews coded via Atlas.ta.8 software. The research data were analysed using an open coding method. The results of the research were presented in 46 categories and 6 key dimensions.

Many behavioral biases affect investors when making decisions.Behavioral distortions divert the investor from rational decisions, and as a result, resources are not diverted to appropriate investments.Behavioral biases can be evaluated in different types including the two phenomena of mental anchor and collective behavior of investors. In the present study, the phenomenon of collective behavior and mental anchor of investors with emphasis on significant weaknesses in internal control has been investigated. For this purpose, the data of 125 companies listed on the Tehran Stock Exchange during the period 2012 to 2018 were used.Data analysis was performed in eviews software version 10 and in order to fit the regression models,the generalized least squares method of panel data was used. The results using univariate analysis showed that there is no significant difference between the amount of behavioral biases in each of the two groups of companies with weakness and lack of weakness in internal control.While multivariate analysis of data in the form of regression models showed that the auditor's report on significant weaknesses in internal control,(which Weakness indicates high agency costs and high information asymmetry between managers and shareholders) strengthens It is the collective behavior of investors but it has no effect on their mental anchor.According to the results, it seems that the behavioral biases of investors are conditioned by the priority and importance of good and bad news in the market.As long as there is bad news in the stock market towards the company, good news can’t cause behavioral bias in investors.

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.

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.