درخت حوزه‌های تخصصی

Online social media is an important innovation that has attracted individuals and companies. The purpose of this study is to "develop an online social media marketing model about consumer buying behavior". This utilizes a mixed-method (qualitative and quantitative). This study uses the research literature to extract the indicators, then, finalizes them by the Delphi technique in three different rounds. The study develops the qualitative model of the research based on the opinions of 20 experts as well as the application of Interpretive Structural Modelling (ISM) and MICMAC analysis. In the quantitative part, the study uses the Partial Least Squares (PLS) technique to implement SEM, validate, and test the predictive relevance of the initial model. This study applies Cohen's formula and determines 460 individuals as sample. The sampling method is non-probability. Furthermore, the statistical population of the study consists of online social media users from all over Iran. In this study, 9 key factors have been identified, modeled and analyzed. According to the MICMAC analysis results, perceived security (with the highest driving power) is identified as the driving indicator, and value co-creation (with the highest dependence) is identified as the result or target indicator. The ISM based model is validated using SEM and 15 hypotheses were checked using SEM-PLS technique, also all hypotheses except the second hypothesis were confirmed. Finally The findings confirmed Goodness of Fitness and ideal predictive relevance of the model.

Safety policies are implemented in the organizations to reduce occupational accidents and prevent waste of resources. Numerous measures will reduce the failure to implement occupational safety policies. In this research, these factors are identified and ranked.This research is applied and adopts survey as its strategy. The research population consists of 15 Safety managers in Kerman province who are selected through non-random and purposive sampling. Data collection tools are interviews and questionnaires and FMEA technique and integrated approach of AHP and fuzzy TOPSIS are used to analyze the data. Based on the findings, important and effective measures to reduce the failure of safety policies implementation include reducing the change of managers and requiring new managers to implement safety policies, developing scientific programs in accordance with the organization's capabilities.According to the research findings, it is necessary for organizations to pay more attention to creating and strengthening the structure of safety units by carefully planning and changing the approach of managers.

In recent decades, lifestyle has been one of the most important concepts in marketing, and management studies. Hence, this study aimed to identify the values and lifestyles of Iranian consumers. According to Cochran’s formula, 1580 people were selected as the statistical sample. Eighteen values were identified by examining the theoretical foundations and interviewing 77 Iranian citizens. The data collected by Likert-scale questionnaire were evaluated using R software, clustering method. Considering the importance of Iranian values, three clusters were identified: “normal lifestyle,” “phlegmatic lifestyle,” and “ideal lifestyle.” This study is one of the first attempts to create a general typology of Iranian consumers based on values and lifestyle and focusing on cultural, ideological and belief differences of Iranian consumers. Researchers are suggested to take advantage of the lifestyles identified in this research in order to identify and deeply understand the consumption behaviors specific to each style in different markets.

Personality is one of the most important study structures in organizational behavior that can play an important role in predicting human behavior. To describe the differences in personality, emotional, and social behavior, researchers have proposed the theory of five personality factors, known as the Big Five. One of the decisions investors make is to allocate wealth to financial and non-financial assets. The importance of this decision is especially evident in financial assets due to their nature and the need for more knowledge and expertise in this field because the mistakes of investors in this regard can create many risks and challenges for them. The main purpose of this study is to explain the effect of personality traits and demographic features on the risk-taking behavior of investors in the Tehran Stock Exchange. The present research is applied research in terms of goals and descriptive-survey research from the method view. The statistical sample of this research includes 358 investors of the Tehran Stock Exchange during 2019 and 2020. Research data were collected based on Sivarjan's (2018) questionnaire. The results of data analysis using the structural equation modeling method indicate that personality traits including neuroticism, extraversion, openness, agreeableness, and conscientiousness have a positive and significant effect on risk-taking behavior. Still, demographic characteristics including gender, age, marital status, and education do not have a significant effect on risk-taking behavior.

The purpose of this research is to Developing a dynamic model for the impact of Servicescape on customer experience in the hotel industry. The present study is a mixed research method. In the qualitative part, the strategy is qualitative content analysis and in the quantitative part, it is applied in terms of purpose and descriptive-survey in terms of research method. To gather information in the qualitative section a collection of articles, books and thesis has been used systematically and in the quantitative part, a standard questionnaire was used to collect information. 700 loyal customers of five-star hotels in Northwest of Iran were selected for the study. A clustered sampling method was used to select statistical sample. In qualitative part, To analyze the data of this research, special content analysis processes including open coding,, axial coding were used to emerge concepts and categories. In the quantitative part, the research hypotheses were tested using structural equation modeling technique. The results of the research showed that the substantive staging and communicative staging have a positive impact on the customer experience and the effect of communicative staging on customer experience at different stages of the relationship life cycle is different.

Countries have made significant investments in the last decade to reach the international market of renewable energy. Studies potentially have indicated a beneficial renewable energy market. Accordingly, firms must alter their business model to enter the global market. This research aims to discover strategic factors in international marketing management for new renewable energy products. The community of this research includes experienced managers and experts in the field of renewable wind energy. The analysis is applied in terms of purpose and is of the qualitative-quantitative method. In this study, structural equation modeling and Smart PLS software have been utilized for data analysis. The results indicate that 11 strategic factors could influence the international marketing management for new wind renewable energy products, and their effect is at a significant level. Finally, the research model was analyzed using fuzzy inference system (FIS) and Matlab software.

Due to the rapid changes and developments caused by technology in the digital age and the growing importance of achieving sustainability for the survival of businesses, the need to create the capabilities needed to innovate in the business model of organizations has become inevitable. This research is trying to provide a model for business model innovation with the aim of sustainability by considering the external factor of digitalization (digital transformation) and the internal factor of dynamic capabilities. The method of this research is mixed so that in the first stage the qualitative method of Meta-Synthesis is used and in the second stage the quantitative method of Interpretive Structural Modeling (ISM) is used. In this research, based on the systematic review of previous researches in scientific databases, 402 related articles were identified and then 46 final articles were selected during the screening process. Based on the thematic analysis, a total of 4 main categories and 14 factors were extracted. In the second stage of the research, an interpretive structural model (ISM) is developed to show the inter‐relationship of different factors and their levels of importance in the food industry.   The 8-level hierarchy model of factors influencing the sustainable business model innovation in the digital age with a dynamic capabilities approach was proposed. Results show that a new component called network value has been added to the traditional business model components. Furthermore, the MICMAC approach has been used to categorise factors according to their driving power and dependence. finally, digital technologies and Sensing were found to be the factors with the highest independence and the highest driving power, and “Economic Sustainability” was identified as the factor with the highest dependence and the least driving power

Achieving e-commerce success is influenced by a variety of interrelated activities. Identifying, analyzing and prioritizing these activities enable decision-makers firstly to evaluate the current success state of Electronic Commerce (EC) businesses more accurately and then to develop an efficient improvement plan to allocate limited resources to achieve a higher level of success. In this research, a Fuzzy Cognitive Maps- analytical hierarchy process (FCM-AHP) approach is used to address this issue by (1) determining interrelationships between influential activities, (2) determining how much these activities influence each other and the overall success, and (3) prioritizing activities to develop a sound improvement plan. In this approach, the FCM technique is used to indicate all possible causal interrelationships between activities and consider all feedback loops between them. Then, the AHP technique is used to determine the contribution weights of activities to the overall success. The obtained management matrices that make it possible to categorize influential activities and the results of improvement scenarios help the decision-makers to develop the most efficient improvement plan and gain the maximum benefit from the allocation of limited resources. An empirical study is implemented to serve as an example to indicate how the approach works.

The most important aspects of image processing are image enhancement. The visual form of an image can be enhanced by using image enhancement techniques for better human interpretation. In this research paper we discuss an   outline and analysis of commonly used image enhancement techniques using finger vein image or personal authentication. Also, experiments are carried out to compare performance of various types of filters for removal of noise from the noisy images through evaluation performance parameter such as mean square error (MSE), peak signal to noise ratio (PSNR) values and structural similarity (SSIM). It was found that application of max filter technique ensures an improved quality of the finger vein image. The mean filter is most advanced in de-noising the images. Mean filter is most efficient in eliminating the salt and pepper noise. From the experiments performed on finger vein image using SDUMLA-HMT database, it is proven that Weiner filters are outstanding for elimination of Gaussian Speckle and Poisson noises and thus, Weiner filter is found to be most appropriate and well-suited for eliminating nearly all types of noise.

Internet of Things could be current paradigm, with promising recent wireless communication. Simultaneously as its will likely deliver compelling and effective arrangement, security of the gadgets and organization could be challenge issue. Therefore security has arisen as quite difficult for the IoT during this paper is expect to create security challenges of the IoT, structural design of IoT through handling security troubles at every layer of the design, key advancements in IoT and use of IoT.

This paper presents an approach that uses both genetic algorithm (GA) and fuzzy inference system (FIS), for feature selection for descriptor in a quantitative structure activity relationships (QSAR) classification and prediction problem. Unlike the traditional techniques that employed GA, the FIS is used to evaluate an individual population in the GA process. So, the fitness function is introduced and defined by the error rate of the GA and FIS combination. The proposed approach has been implemented and tested using a data set with experimental value anti-human immunodeficiency virus (HIV) molecules. The statistical parameters q2 (leave many out) is equal 0.59 and r (coefficient of correlation) is equal 0.98. These results reveal the capacity for achieving subset of descriptors, with high predictive capacity as well as the effectiveness and robustness of the proposed approach.

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.