درخت حوزه‌های تخصصی

The purpose of this study was to investigate the effect of business intelligence, foreign market knowledge and entrepreneurial characteristics on the speed of internationalization with the mediating role of organizational agility in SME pharmaceutical companies in Alborz province. The statistical population of this research was formed by those who hold a position and responsibility in SME pharmaceutical companies. In fact, they are decision makers. Therefore, it is possible to consider those who are part of the managerial positions, assistants, supervisors, senior decision makers, board members, and experts of different departments of SME pharmaceutical companies of Alborz province as members involved in the statistical population. did In this research, Klein's approach was used to determine the sample size. He believes that 10 or 20 samples are necessary for each variable. Considering the presence of 8 variables in the research, the sample size is estimated to be 20 times that is 160 samples. Non-probability sampling method was used. A researcher-made questionnaire was used to collect data and its Cronbach's alpha coefficient was 0.950, which indicates the reliability of the research tool. Also, the KMO test was confirmed in the validity of the research instrument construct. Smart PLS software was used to analyze the data and test the research hypotheses. The research findings out of the ten mentioned hypotheses show the confirmation of seven hypotheses and the rejection of three hypotheses. The foreign market knowledge construct did not show any positive and significant relationship with organizational agility structures and the speed of internationalization and also its mediating role was not confirmed. But other structures, namely business intelligence and entrepreneurial characteristics, showed a significant and positive relationship with organizational agility and the speed of internationalization, both directly and indirectly through the mediating role of organizational agility.

In attempts to examine the mapped spaces of a literary narrative, various quantitative approaches have been deployed to extract data from texts to graphs, maps, and trees. Though the existing methods offer invaluable insights, they undertake a rather different project than that of literary scholars who seek to examine privileged or unprivileged representations of certain spaces. This study aims to propose a computerized method to examine how matters of space and spatiality are addressed in literary writings. As the primary source of data, the study will focus on Viet Thanh Nguyen’s The Sympathizer (2015), which explores the lives of Vietnamese diaspora in two geographical locations, Vietnam, and America. To examine the portrayed spatial relations, that is which country is privileged over the other, and to find out the underlying opinion about the two places, this study performs topic modelling with Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) by using TextBlob. In addition, Python is used as the analytical tool for this project as it supports two LDA algorithms: Gensim and Mallet. To overcome the limitation that the performance of the model relies on the available libraries in Python, the study employs machine learning approach. Even though the results indicated that both geographical spaces are portrayed slightly positively, America achieves a higher polarity score than Vietnam and hence seems to be the favored space in the novel. This study can assist literary scholars in analyzing spatial relations more accurately in large volumes of works.