درخت حوزه‌های تخصصی

Purpose: The purpose of the current research is to extract the components and dimensions of digital transformation and determine the levels and sequence of dimensions in Iran's education and upbringing, for the planning and implementation roadmap.Method: This research has a mixed approach (qualitative-quantitative). The qualitative section was able to extract the components of digital transformation by searching for authentic Persian and English articles published in the last ten years. With the help of seven experts, the digital transformation components were classified into eight dimensions in the form of 109 identified sub-components using the ISM method. Dimension levels were determined by SSIM matrix calculations in the quantitative part. MICMAC software version 5.3.0 was used to calculate the impact of relationships.Findings: Based on the opinion of experts and the results of ISM calculations, the identified dimensions including: institutional, education, technology, infrastructural, social, cultural and economic were placed in four levels.Conclusion: According to experts, the institutional dimension, being placed at the fourth level, has the greatest impact on digital transformation in Iran's education. Therefore, it is one of the key dimensions to achieve digital educational transformation that is placed at the first level of the model. According to the results of ISM, the economic and cultural dimensions were placed at the third level. Also, in analyzing the impact of relationships between dimensions using MICMAC software, the results indicate that the institutional dimension has a significant impact on infrastructure development.

Consumers rely on social media opinions to make product choices and purchases. With the popularity of web-based platforms like Tripadvisor, consumers express their opinions and feelings about food quality, service, and other aspects affecting restaurants through comments. Hence, analyzing these comments can be valuable for others to choose a restaurant or to improve and develop their products and brands. Sentiment analysis utilizes text mining methods to extract, identify, and study emotions and subjective perceptions. Since consumers can use comments to choose a restaurant, this study seeks to provide sentiment analysis of their reviews on the Tripadvisor website about Iranian restaurants. This study is applied in nature, aiming to analyze and manually label 4000 comments from the Tripadvisor website regarding restaurants in ten tourist cities across Iran. It uses a standard extended long short-term memory algorithm for sentiment analysis, a deep learning neural network, and Python text mining packages for modeling. The results indicate that the F-Measure for all aspects exceeds 80%, indicating sufficient efficiency and accuracy of the aspect-based sentiment analysis model for restaurant reviews. The most significant features for customers of Iranian restaurants are the food and the atmosphere. This study represents one of the initial efforts to analyze comments posted on the Tripadvisor website concerning Iranian restaurants. Business owners in the tourism industry, especially restaurant owners, can use the proposed model to automatically and quickly analyze customer feedback, improve performance, and gain a competitive edge. The proposed model can also assist users of online platforms in analyzing the opinions of others, enabling them to make informed decisions more efficiently.

Objective: This study aims to evaluate one of Algeria's key strategies for developing an entrepreneurial culture among university students in higher education, which is entrepreneurial education. It analyzes the number of files submitted by university students through the National Agency for the Support and Development of Entrepreneurship from 2013 to 2020, as well as the statistics on students who participated in the Entrepreneurship House from 2013 to 2019. Methods: The study evaluates entrepreneurial education as a strategy for fostering an entrepreneurial culture among university students in Algeria, based on the number of files submitted for establishing micro-enterprises at the National Agency for the Support and Development of Entrepreneurship. It also assesses the outcomes of activities promoting entrepreneurship by entrepreneurship centers in higher education institutions in 2019. Results: The statistics reveal that Algeria’s experience in teaching entrepreneurship in higher education is both recent and weak. This weakness is due to the limited role of educational programs in business plan preparation and their inability to keep up with scientific advancements. These programs also fail to equip students with the necessary knowledge, skills, and capabilities to start their own projects or invest in opportunities for small business creation. Conclusions: Developing an entrepreneurial culture in universities requires collaboration across various efforts. This includes the Entrepreneurship House, which connects students to the economic environment through awareness and training, university business incubators that provide support and funding, and student clubs and research labs that foster an entrepreneurial culture within the academic setting.

This study aims to analyze the factors associated with motivation and attitude towards English learning through the application of structural equation modeling (SEM). A quantitative, ex post fact, cross-sectional and explanatory study was carried out on 1202 university students. Eight motivational factors were examined, namely: Motivational Intensity (MI), Attitude towards Learning English (ATLL), Integrative Orientation (IntO), Instrumental Orientation (InsO), Interest Towards Foreign Languages (ITFL), Evaluation of English Teaching (EET), Desire to Learn English (DLE), Evaluation of the English Course (EEC). The multiple regressions showed that EET, DLE, InsO, and EEC are predictors of the MI. It was found that DLE, InsO, and EEC are predictors of ATLL. The SEM showed a weak positive influence of InsO on DLE, ITFL, EET, EEC, MI and ATLL. IntO had a weak positive impact on DLE, EET, EEC, and ITFL. There was a weak positive impact of DLE on ITFL, ATLL and MI, and it had a strong effect on EEC. Finally, EET had a weak positive effect on MI, whereas EEC had a weak positive impact on ATTL and MI. This research provided information that serves to understand the factors affecting the intensity of motivation and attitude in L2 learning

A developed financial system, which ensures the provision, allocation, and direction of resources toward productive economic sectors, is one of the key drivers of economic growth and development. On the other hand, given the low efficiency of various economic, productive, and social sectors, attention to total factor productivity (TFP) is fundamental for global competitiveness. Given resource constraints in the economy, improving this factor can enhance investment, optimize resource allocation, prioritize productive sectors, reduce costs, improve product quality, and increase value added in economic sectors. Accordingly, the present study aims to investigate the impact of credit on total factor productivity and the growth of value added in Iran’s economic sectors using the Panel ARDL approach and quarterly time series data from 2012Q1 to 2024Q4 (Persian calendar). The results indicate that real credit, real exchange rates, and real capital formation in machinery have a positive and direct impact on the growth of value added and total factor productivity in economic sectors. Specifically, credit in the industrial sector and exchange rates in the service sector exert the greatest influence on TFP and the growth of value added. Additionally, capital formation in machinery in the agricultural sector has the most significant impact on total factor productivity.