درخت حوزه‌های تخصصی

Purpose: The purpose of this study is to design an organizational performance model based on the digital status of the organization in the Corona pandemic with a theme analysis approach in the Ministry of Industry, Mine, and Trade (MIMT).Method: This research was conducted with a qualitative approach. The research tool (data collection) was a semi-structured interview in which 27 elites and qualified specialists in MIMT (statistical population of this study) were selected using a purposive sampling approach to qualitative sampling.Finding: The results of data analysis in the qualitative section in the open coding stage identified 79 codes, which finally through axial coding achieved  3 categories (main category) and 21 indicators (subcategory).Conclusion: The main mission and goal of the managers of any organization is the effective and efficient use of various resources and facilities such as labor, capital, materials, energy, and information. Digital transformation, which has always been important as a fundamental and strategic action in organizations, has become more important in recent years following the outbreak of coronavirus; the pressures of the coronavirus epidemic have accelerated the efforts of organizations to create digital transformation

Purpose: Technology plays an essential role in any country to achieve sustainable development. Choosing appropriate and advanced technology is one of the strategic decisions of industrial managers. This research discusses the critical factors that should be considered in the process of choosing new technologies based on the competitive mechanism, and accordingly, designs and explains the technology selection model for equipping downstream petrochemical industrial units to create a competitive advantage.Method: The research method is an applied one and in terms of data type, it is a partly qualitative and quantitative research. In the quantitative phase, research data was collected based on the data obtained from the qualitative phase, and using the questionnaire tool. The research data was analyzed using thematic analysis and MaxQda software in the qualitative phase and they were also  analyzed using the TOPSIS technique and meta-heuristic algorithm in the quantitative phase for modeling.Findings: Thirty-four concepts and five categories were identified as technology selection criteria for equipping downstream petrochemical industrial units to create a competitive advantage. The "technical criteria" factor was identified as the foremost priority in equipping downstream petrochemical industrial units to create a competitive advantage. The ranking of the determined categories from one to five were the factors of competitive criteria, strategic criteria, internal criteria, and external criteria, respectively.Conclusion: Finally, using the TOPSIS fuzzy technique, the model of equipping industrial units was presented to help decision-makers and policy makers of downstream petrochemical industries.

Bali tourism faced conflict with the local community because of the utilization and commodification of sacred and forest areas. The first sacred area conflict occurred in 1994, based on the use of the sacred land “Tanah Lot” become tourists resort namely Bali Nirvana Resort (BNR). Furthermore, this problem is presently observed in the utilization of mangrove forests for the international airport and LNG (liquefied natural gas) terminals, respectively. In these conflicts, all facilities are designed to support the increase in the inbound tourist visits and foreign investment. This growth of Bali tourism threatens the development of sustainable tourism, which concerned with environment, local communities and local economy, but spiritual tourism creates the conflict subsided due to the dependence of spiritual tourism on sacred areas, the environment, and local community resources. Therefore, this study aimed to evaluate Corporate Social Responsibility (CSR) of spiritual tourism in building sustainable economy based on the local wisdom of Bali Tri Hita Karana. Data were qualitatively analyzed and obtained from literature reviews, observations, and in-depth interviews. The results showed that spiritual tourism emphasized sacred areas, the surrounding natural environment, and local community assets. This dependence developed a mutually beneficial relationship, subsequently leading to the CSR of Spiritual Tourism. The CSR based on the local wisdom Tri Hita Karana which create harmonious between the local people, environment, and investment.

The current research mainly aims to investigate the effective factors in creating a food tourism brand in Shiraz. The statistical population includes all experts in the field of tourism and hotel management, food, cooking, and restaurant industry, and tourists, who were selected using the purposeful sampling method. First, 13 factors were identified according to research literature. The identified factors were given to 10 selected experts who were aware of the desired criteria. Then, the relationships between these factors were determined using the fuzzy Dematel method, and the answer to the second question was also determined. The results showed that the factor of investment in the field of food tourism, and using new advertising tools in this field are the most important thing in creating a food tourism brand in Shiraz. The parameters of food quality and food authenticity are the most effective in this area. In addition, creating a good feeling towards the brand and advertising, in this field are among the most effective parameters, respectively. Food tourism branding issue is the most important issues that should be considered in Shiraz. In this regard, it is possible to identify the foods of Shiraz as intangible cultural heritage, bring them to the national register, prepare their files for global registration in UNESCO, and conduct cultural and political consultations so that the files should be presented at the international level.

Internet-based social channels have turned into an important information repository for many people to get an idea about current trends and events happening around the world. As a result of Abundance of raw information on these social media platforms, it has become a crucial platform for businesses and individuals to make decisions based on social media analytics. The ever-expanding volume of online data available on the global network necessitates the use of specialized techniques and methods to effectively analyse and utilize this vast amount of information. This study's objective is to comprehend the textual information at the Lexical and Semantic level and to extract sentiments from this information in the most accurate way possible. To achieve this, the paper proposes to cluster semantically related words by evaluating their lexical similarity with respect to feature and sequence vectors. The proposed method utilizes Natural Language Processing, semantic and lexical clustering and hybrid C4.5 algorithm to extract six subcategories of emotions over three classes of sentiments based on word-based analysis of text. The proposed approach has yielded superior results with seven existing approaches in terms of parametric values, with an accuracy of 0.96, precision of 0.92, sensitivity of 0.94, and an f1-score of 0.92.

Microfinance institutions in India provide a set of financial services to the economically weaker sections. Recently, a large number of microfinance institutions have emerged in India and they have favorable impact for poverty reduction. The impact of these institutions on entrepreneurship and society, needs to be explored in greater depth. The objective of this study is to apply machine learning techniques to explore this impact. The research uses a MIX dataset for three successive years, namely 2017, 2018, and 2019. This dataset comprises eight variables centered on gross loan portfolio. Principal Component Analysis (PCM) has been applied on the sample dataset for dimensionality reduction, resulting in two main components and each component consist of fraction from eight variables. Then, the sample dataset has been labelled with the help of clustering using K-means clustering technique. Further, classification models based on K-Nearest Neighbors (KNN) algorithm and Support Vector Machine (SVM) are applied to predict the appropriate category of entrepreneurship. The experiment result shows that the machine learning techniques have been found effective and useful tools for estimating the impact of microfinance on entrepreneurship in India.