دانشنامه تحول دیجیتال
سال دوم شماره 2 (پیاپی 3، تابستان 1400)

Today, with the increasing development of technology and mobile applications of the bank, the desire of people to use mobile banking services has increased due to its unique features. Therefore, banks always try to maintain the quality and security of the bank's mobile applications, to create trust in their customers that encourages them to use mobile banking services. To the extent that mobile banking services are of high quality and consumers trust them, consumers' satisfaction and intention to use these programs will increase, which will affect their actual use of these services.The present study is based on positivist philosophy and the research approach is deductive. The research strategy is descriptive and based on the selection of the research is quantitative and the research period is short-term and the data collection method is a questionnaire. SPSS16 and SmartPLS3.0 software were used to analyze the collected data. The results showed that the quality of services has a significant effect on satisfaction and intention to use mobile banking services. The quality of information provided does not have a significant effect on satisfaction and intention to use. The quality of the system has an effect on satisfaction but does not have a significant effect on the intention to use it. Trust has a significant effect on satisfaction but does not have a significant effect on intention to use. On the other hand, satisfaction has a significant effect on the intention to use and actual use.

The aim of this research was to measure the satisfaction of students with the e-learning system during the outbreak of Corona pandemic. In this study, after conducting library studies and existing documents in the study community Factors affecting students' satisfaction were extracted from the e-learning system and research hypotheses were designed based on it. A researcher-made questionnaire was used to test the research hypotheses. In this questionnaire, the validity of the factor analysis method and the reliability of the Cronbach's alpha coefficient were confirmed. The reliability test of the questionnaire was performed for 51 samples and the results showed acceptable reliability. The questionnaire was distributed electronically through an e-learning system among a sample of 307 people obtained from the Cochran's formula from the study population who were students of Mehralborz University in 1399. Structural equation analysis method was used to analyze the data and the results showed that the model generally fits well. And the dimension of environment has the most effect on perceived satisfaction, followed by student dimension, user interface design, professor and technology, respectively, and the flexibility of educational units has the least effect on perceived satisfaction.

Since digital transformation as a new phenomenon in the current era leads to the technological and strategic superiority of organizations, in this research, using a systematic review of research literature and data theory of the foundation and MAXQDA10 software, the components, and dimensions of maturity of digital transformation in Iran have been identified. The information population of the study is a research that was conducted in Iran between 1398 and 1400, in which 204 articles were extracted in the initial stage of search in domestic scientific databases. After various screenings, 16 articles were selected as the definitive research source. After analyzing the information and performing various stages of open, axial, and selective coding, 467 primary codes, 219 secondary codes, 37 concepts, 11 categories, and 3 macro dimensions were obtained. results showed that the digital transformation maturity model has three major dimensions and eleven categories: infrastructure (including managerial, technical, human, and cultural infrastructure), digital transformation leadership (including roadmap, governance, organization, and digital resources), and digital senior management (including Digital planning and guidance, digital coordination and actions, and environmental monitoring and digital networking). to validate the codings, the agreement method of two coders was used using the kappa coefficient. its value was equal to 0.62 which was more than its threshold value of 0.6 and indicated the acceptable validity of the conceptual model Provided. the codification and the degree of effectiveness of the criteria about each other in the conceptual model of the research were confirmed by a survey of validation experts.

Along with the digital evolution and the emergence of new technologies such as the Internet of Things, some factors such as expansion, agility and management of infrastructure and communication networks has also developed alongside with software, service orientation, open source and virtualization approaches, so that the development of Software Defined Networks (SDN) has become one of the main pillars of the digital transformation journey of telecom companies. In this study, we have evaluated the readiness of applying software-based networks in the digital transformation journey of Iranian telecom companies. The data of this research has been provided through a questionnaire from 54 experts in digital telecom transformation in Iran in 2021. As for the research methodology, Confirmatory factor analysis, one-sample Wilcoxon test and Friedman test were used to test the research hypotheses. The results show that the existing concerns are mostly in the fields of security, suppliers and experts, but there is a positive trend in accepting software-based networks. Also, the findings of this study indicates that the attention to the benefits of software-based networks in the telecom industry (5/99) is in the first priority and the main factors of applying this technology (5/97), business impacts (4/56) and barriers to acceptance (4/5) come next.

The present study was conducted using a descriptive-analytical method. The statistical population of this research is experts familiar with the subject, which includes university professors and experts in the field of crisis management. The sample number is 8 experts who were interviewed in this study through a pair of questionnaires and a range of 1 to 9. To check the validity of the measuring instrument or the paired questionnaire, content validity was used and the questionnaire was provided to professors and experts to confirm the accuracy of the criteria and sub-criteria. In purposive sampling of the present study, purposive judgmental sampling has been used, which means that a limited number of people have the appropriate information to answer the research questions. Experts in this study were people who had at least 8 years of experience or research in crisis management. Sampling continued until the theoretical saturation stage. Super Decision software and ANP network analysis process were used to prioritize the criteria. The results showed that SC8 sub-criterion, which is "planning and preparation" and belongs to the telemedicine criterion (C3) with a weight of 0.571, has the most importance among the factors affecting management effectiveness. The crisis is with the emphasis on the use of information technology. It is followed by SC10, which is a monitoring, forecasting and early warning system, with a weight of 0.432. This sub-criterion belongs to the criterion of the role of wireless sensor networks and unmanned aerial vehicles (C4).

The main reason that data mining has become the focus of attention in the information industry is the availability of large volumes of data and the urgent need to extract useful information and knowledge from this data. In data cleaning operation, the problem of data quality is solved. One of the problems that affects the quality of data is skewed data or abnormal data. These are records whose attribute values are very different from other records. In this research, a method based on deep learning and 14-layer deep neural network on the tensorflow and cross package has been used to diagnose the abnormality and improve its performance. The data set used in this research is a data set with 2% anomalies. The accuracy of the proposed method was 97.08 and the readability and accuracy criteria were 97%. The proposed method was compared with 5 other models based on convolutional neural network and LSTM recursive network. The value of the classification evaluation criteria showed a very good improvement over the proposed method compared to traditional methods and even methods based on deep learning.