درخت حوزه‌های تخصصی

The increase in Internet technology popularity due to the advancement of information technology is bound to rise at an unprecedented pace in China. The development of E-commerce has become an important part of people everyday lives in certain sectors of business activities such as online advertising, online shopping, online promotional advisory and service. This study examines critical factors in online shopping that affect customer satisfaction. The philosophy of positivism was used to evaluate the online shopping behaviour in a Chinese context. The philosophy defines a structural method that combines logical deduction with accurate and well-known observation of personal character and behaviour. The data on the level of customer satisfaction collected by E-mail and survey. The results of the validity and reliability test, Pearson's correlation and Multiple Linear Regression analyses suggested that online shopping websites’ privacy, delivery system and service quality would improve customer satisfaction in China. The Companies must carefully develop the E-commerce environment in terms of customer behaviour in China, emphasizing the factors that have a significant impact on their online customer satisfaction

Accelerating digital transformation and the basic needs of businesses to adapt to environmental transformations and complexities have doubled the necessity for extensive stakeholder interactions. Gamification is a powerful tool that facilitates stakeholder interaction and a common understanding of the vision and contributes to the sustainability of businesses. Businesses' sustainability in the digital transformation age depends on the continuous interaction of stakeholders and a comprehensive understanding of all the business pillars, which will be possible through the flow of ideas inside and outside the workplace and by providing innovative processes. In this regard, this study explores the effect of gamification on corporate sustainability by explaining the role of digital transformation, open innovation, and value co-creation. The statistical population of this study comprises top managers and experts in e-businesses who use gamification in their processes. The statistical sample included 117 managers and experts active in this field, and they were selected through convenience sampling. The data collection tool was a questionnaire reliability of which was 0.763 using Cronbach's alpha. Using Smart PLS software, the gathered data were analyzed by structural equation modeling (SEM). Gamification, with a factor of 74%, has a positive effect on open innovation, while a factor of 85% has a positive effect on digital transformation. Gamification, both directly and indirectly, creates value co-creation in businesses, and finally, value co-creation at 78% affects corporate sustainability.

The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated where it considers eventuality. So, it is necessary to consider the highly data-driven technologies and to use new methods of analysis, like machine learning and visualization tools, with the ability of interaction and connection to different data resources with varieties of data regarding the type of big data aimed at reducing the risks of policy-making institution’s investment in the field of IT. The main scientific contribution of this article is presenting a new approach of policy-making for the now-casting of economic indicators in order to improve the performance of forecasting through the combination of deep nets and deep learning methods in the data and features representation. In this regard, a net under the title of P-V-L Deep: Predictive Variational Auto Encoders - Long Short-term Memory Deep Neural Network was designed in which the architecture of variational auto-encoder was used for unsupervised learning, data representation, and data reconstruction; moreover, long short-term memory was adopted in order to evaluate now-casting performance of deep nets in time-series of macro-econometric variations. Represented and reconstructed data in the generative network of variational auto-encoder to determine the performance of long-short-term memory in the forecasting of the economic indicators were compared to principal data of the net. The findings of the research argue that reconstructed data which are derived from variational auto-encoder embody shorter training time and outperform of prediction in long short-term memory compared to principal data.

In the digital age, e - learning systems have been employed as new equipment in the higher education system in different universities. Consideringthe importance of optimization of this system, this research is aimed at providing a modelfor the effectiveness of e - learning at in higher education systems . The study is a descriptive survey study in terms of its data collection method . The population includes all the students of electronic courses at the University of Tehran . This population includes 1481 students of the University of Tehran in the academic year 2019 - 2020 . Regarding the population size, 300 students were selectedbased onstratified sampling, using Cochran ’ s formula.Lisrel and Amos softwarewere used for data analysis . In the first step, byliterature review, and based on the collected information, 87 components were identified to be related toe - learning effectiveness . Then, based on the highest frequency of the identified components in one hand, and their significance from the experts ’ viewpoints, on the other hand, 14 components were finally selected and classified in three major classes including;pedagogical, individual and technicalrelated factors.