List of Articles internal and external factors

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  1 - Exploring and Ranking the Intellectual Capital Factors Affecting Knowledge-Based Companies (Case Study: Knowledge-based companies in North Khorasan)
  masomeh mohammadabadi kioumars niazazari negin jabbari
  This research was conducted with the aim of exploring and ranking the intellectual capital factors affecting knowledge-based companies. The research was performed using a descriptive-survey method with a quantitative approach. The statistical population of this research More

  This research was conducted with the aim of exploring and ranking the intellectual capital factors affecting knowledge-based companies. The research was performed using a descriptive-survey method with a quantitative approach. The statistical population of this research consisted of 155 directors of knowledge-based companies, from whom 110 people were selected as samples using simple random sampling method. The data gathering tool was a researcher-made questionnaire . Opinions of experts and professors were used to ensure the formal validity of the questionnaire. The reliability of the questionnaire was estimated to be 0.89 using a composite reliability criterion model. Data was analyzed using SPSS20 software and SMART-PLS software package by confirmatory and exploratory factor analysis. The results showed that the intellectual capital components that affect knowledge-based companies can be divided in two groups, internal factors (entrepreneur characteristics, company strategy and characteristics of the company) and external factors (institutional environment, political environment, cultural/social environment, economic environment and the infrastructure environment). The final model of the intellectual capital factors influencing knowledge-based companies was confirmed by fitness indicators with an index value of 0.70 which for the designed model, shows a suitable fitting of the factor structure and the theoretical basis of the research. Manuscript profile
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  2 - Application and Comparison of Simple Additive Weighting method, Fuzzy Analytic Hierarchy Process and Support Vector Machine in identifying the internal and external factors in SWOT’s analysis
  Ali HaeriaAn Ardekani Hamidreza Koosha fatemeh mirsaeedi
  All organizations, must determine their future path; in other words, they must understand where they stand and where they are heading to. Strategic management is one of the most recognized management approaches for this purpose. One of the most important steps in strate More

  All organizations, must determine their future path; in other words, they must understand where they stand and where they are heading to. Strategic management is one of the most recognized management approaches for this purpose. One of the most important steps in strategic management is recognizing organization’s internal and external factors. If these factors are recognized correctly, they can be used to establish correct and optimal strategies. So far, few researchers have used exact methods for identifying and prioritizing internal and external factors. In this article, we try to use multi criteria (Sample Additive Weighting and Fuzzy Analytic Hierarchy Process techniques) and data mining (support vector machine) for reorganization of internal and external factors. The case study in this research is Water and Sewerage Company of Mashhad. First, organization’s internal and external factors are identified and classified by organization’s higher managers and experts. For applying Sample Additive Weighting and Fuzzy Analytic Hierarchy Process, first, the criteria according to internal and external factor’s definition are determined and criteria’s weights are identified  by Fuzzy Analytic Hierarchy Process also Sample Additive Weighting. Then, by using these weights, the values for all factors are calculated and classified. Using these criteria (attributes) and WEKA software, after data preprocess, factors classified by Support Vector Machine that is one of the most accurate data mining approaches. The results show Support Vector Machine prediction more accurately compared to other techniques. Manuscript profile