user satisfaction

With the start of the Coronavirus epidemic, remote education systems were widely accepted in all universities around the world. The University of Tehran, the top university in Iran, had an electronic education system since 2010, which provided services in this field to the academic community. With the spread of the pandemic, these services became widespread in all departments of the University of Tehran. Compared to traditional learning, E-Learning brings challenges for Users, the main challenge of which is Users’ Satisfaction. In this paper, by examining the developed model based on the TAM model and the factor analysis of this model, the level of satisfaction of student users with the elearning management system of the University of Tehran during the Corona era is carefully evaluated. The exploitation of this model with the analytical method of partial least squares in the construction of structural equations is based on the information collected from the questionnaire that has been provided to the users of the e-learning system of the University of Tehran. The results obtained from this study show that the Technology Acceptance Model (TAM) describes well the factors affecting the level of user satisfaction with the E-learning management system. The positive effect of the visual beauty and the quality of the information in this system has improved the technical quality of the service; as a result, the level of user satisfaction in using this system has increased.

User satisfaction and loyalty are very important in the mobile communications market because mobile is frequently updated. It is very necessary work that builds up a scientific assessment method to assist product in understanding and knowing well the trend of customers. This paper is intend to build up a scientific assessment method for measuring user satisfaction and loyalty. First, combining the group decision making and TOPSIS (technique for order preference by similarity to ideal solution) technique, a theoretical framework of evaluation method is established. Second, the respondents are allowed to express their opinions by using some simple symbols or by leaving the lack of answers to some measurement questions, even whole questionnaire. Then the symbol information along with the nonresponses in questionnaires are fused into an intuitionistic fuzzy information. Third, the levels of user satisfaction are ranked based on TOPSIS technique and projection measure in an intuitionistic fuzzy environment. Finally, the theoretical and practical implications of current model are discussed, the important limitations are recognized and future research directions are suggested.

In today's world of numerous sources of multimedia content, recommender systems help users find relevant content items. In our research the reasoning behind the recommendations generated by such systems was explored to check whether presenting users with explanations of recommended content increases their trust in the system. A content-based recommender for TV content has been developed which focuses on items attribute values. The system predicts user's ratings by classifying the vector of similarities between the user model and the items attributes. User's trust is increased by identifying attribute values that are the most relevant for them. User's feedback to the identified attribute values was used to improve the performance of the recommender algorithm. Tests in our experimental platform showed that the developed algorithms produce good results. The accuracy of the system was around 75% in the basic version and it further increased in the enhanced, while the identification of relevant attribute values achieved 86% precision.