فصلنامه مطالعات مدیریت کسب و کار هوشمند
پیاپی 35 (بهار 1400)

Customer satisfaction requires the customer to be happy both in daily and long-term and global interactions. People's opinions about the products of a company on websites and social media can provide useful information for companies to evaluate customer satisfaction. In this research, using the methodology text mining and k- means clustering, customers' opinions about the three brands of Snowa, Pakshoma and Parskhazar from domestic appliances and comments on the three brands of Samsung, LG, and Tefal from external home appliances in the website of Digikala.com were analyzed. The results of this study show that dissatisfaction factors were clustered in six attributes, product failure, and price proportions with performance, efficiency, design, manufacturing quality and after-sales services. In domestic appliances, the most dissatisfaction factors were the product failure, price proportions with performance, manufacturing quality, after-sales service, efficiency, and design. And the factors causing dissatisfaction in external home appliances were manufacturing quality, product failure, design, after-sales service, price proportions with performance, and efficiency.

This paper aims at comparing the banks’ activities in the social networks using the content analysis method. In this study, 54 profiles for 14 external and internal banks in 4 popular social networks are analyzed. The banks are chosen using the purposive sampling method. Results show that most banks use missions/goals on their social page to explain the bank information. They use the augmented product to present the product information. They use the company image to explain corporate identity. They use meetings and conferences to present bank events. They use music style for their videos. They use lifestyle pictures for their photos. They use information support for customer support. They use intimate/ interactive/ harmonious/ poetic style for their slogan and use their campaign albums for their albums. They also use sport and environmental issues to explain their corporate social responsibility. Moreover, they use services as the bank’s marketing messages. They use news announcements to release information. Furthermore, they use social responsibility and public consultation for socialization and finally, they use public partnerships for interactive customer engagement.

Due to the lack of direct communication between teacher and learner in the e-learning environment, learners in this environment need education with good support and personal redemption. Using this research, you can have new technology in e-learning on the emotions and moods of learners. The statistical population of Farzanegan 7 high school math students is 75 people. In order to find 5 different types of learners' emotions, students are divided into 5 groups of 15, each of which is specifically exposed to different conditions. You have to experience happiness, anger, fear, frustration and hatred, and their face information is posted through the webcam. Your videos are recorded and the learners' emotions are measured and detected in different situations according to the neural network's deep learning algorithms by the Face Reader incremental software system. There has been a research method of designing a fuzzy expert system and a fuzzy inference system. And makes learners discover. And reject. Created within ranges. This change indicates that it increases the feeling and increases the negative feeling. Keywords: Internet of Things, e-learning, learners' emotions.

In recent years, information and communication technology has come to the aid of entrepreneurship and a new debate called digital entrepreneurship has emerged. The purpose of this study is to present a conceptual model of digital entrepreneurship development in small and medium knowledge based companies.  After reviewing literature and interviewing with 17 experts in universities and organizations digital entrepreneurship developmental factors were presented that were 147 codes. Using content analysis and focus group methods, these codes were grouped into 29 based content and 7 organizer contents. In the next step, using interpretative structural modeling method, the modeling of the basic themes and the organizing content created and the final model of digital entrepreneurship development in SME knowledge-based companies has been made in three level of contextual, structural and content factors. The results showed that the contextual factors with the most impact and structural factors with the most impressionability, have an important role in the development of digital entrepreneurship in SME knowledge-based companies.

The Present Age is known as the age of knowledge while knowledge-based economy is emerging. So using and developing knowledge management in these circumstances is necessary. Being success in applying knowledge management requaires knowledge management maturity evaluation. This evaluation helps identifying and passing the barriers within knowledge management. There are several models suitable in evaluating knowledge management maturity and we must choose among them. In these models various dimensions have been considered for maturity while all dimensions have an equal weight meanwhile differences among organizations are almost neglected. In this research after finding the basic framework, FANP method has been used and by polling expert opinion the ellements of framework were localized for Ansar Bank while different weights allocated through its dimensions. Finally localized framework has been presented in 5 levels and 54 dimensions in order to set priorities within each layer. This could lead to greater accuracy in determining the banks maturity due to environmental conditions. This method can be used for localizing the evaluation of knowledge management maturity in other organizations.

Creating a great user experience is one of the main goals of online stores, which actually enables them to create a lasting competitive advantage. The user experience is also one of the valuable and innovative sources of information for designing recommender systems. Given the competitive world of commerce and the high similarity of goods and services, and considering the important role of the user experience and actually creating a positive impression in the user's mind, this study aims to determine the backgrounds and consequences of user experience from recommender systems in Online environments are done. The methodology of the present study is synthetic. In the qualitative section, 20 experts were selected through semi-structured interviews through purposeful judgment sampling. Then based on qualitative data content analysis, the initial research model was presented. In the quantitative part of the study, the statistical population of the study included all users and customers of the Digikala store that used its services in March and April 2019. For this purpose, 384 samples were selected by available sampling method. LISREL software was used to analyze the data in a small section and the hypotheses were confirmed. The results indicate that there are five main categories of background factors including perceived impact experience, perceived ease of experience, perceived quality experience, perceived support experience, and perceived external experience. Perceived attitudes, perceived value, perceived trust, and perceived satisfaction were also presented as consequences of the user experience of the recommender system in online environments.

In recent years, the number of users of social networks has grown significantly. The big challenge for these networks’ audience is How to communicate with the people present on these networks. Friend recommender systems try to fix this challenge by offering suggestions. In this study, data from the social and scientific network of Kousarent were used. In this research, using 10 types of relationships between users without considering the friendship relationships, network graph created, and then by using 3 algorithms Louvain, Kmeans and Hierarchical graph clustering was performed to identify communities. Clusters obtained from Louvain's clustering algorithm had higher percentages of matching with friendships. Then, weights were calculated by genetic algorithm for each of 10 relationships and by applying Louvain clustering algorithm on the network graph, the highest percentage of matching with the optimal weight of each of the 10 relationships was obtained. In this case, the resulting clusters are optimal clusters containing the most similar users. So other users in the same cluster can be suggested as friends. The weight of the edges between the individuals in the graph was also used to prioritize the bids. At the end, the friend proposed method was evaluated and the percentage of suggested friends matched with the individual's true friends was calculated.