Abstract Universities are influential and important organizations in society, where large amounts of data and information are produced and used. Considering the importance of using big data technology in universities, it is necessary to check how far Iranian universities have progressed in the subject of big data. The purpose of this article is to evaluate the maturity of big data in Iran's public universities. To achieve this goal, the TDWI model has been used to evaluate the maturity of big data in Iranian public universities. The questionnaire was sent to the entire statistical sample, and finally 50 completed questionnaires were received. Field method and standard questionnaire tools were used to collect data. Face validity was used to evaluate the validity of the questionnaire and Cronbach's alpha coefficient was used to evaluate the reliability.The results of the analysis showed that 2 universities are in the nascent stage of maturity, 27 universities are in the early stage, 16 universities are in the established stage and 5 universities are in the mature stage and there is no university at the advanced level of the maturity model. No relationship was found between the number of university students and big data maturity in universities, and the average level of big data maturity is significantly different between Tehran and city universities.In this study, based on the results, recommendations were provided to each of the universities participating in the survey to promote big data maturity.

Abstract The digital world provides many opportunities for marketers to reach customers. However, in the fast-paced world, finding new and innovative ways to market products and services is very important. Due to the advancement of artificial intelligence and its development in the field of international marketing, professionals now have the tools to completely redefine the current understanding of branding, marketing and advertising. The growing popularity of the Internet and the increased use of mobile devices are generating massive amounts of consumer data that feed artificial intelligence-based systems. This research is a type of mixed research with a qualitative and quantitative approach, which is a survey study in terms of its purpose, application, and in terms of data collection. The statistical population of the research was managers and experts specializing in the field of digital marketing and IT in the international field, who were selected using the snowball sampling method. In the qualitative part, the tools for collecting information were library and articles review, interviews, and in the quantitative part, questionnaires. In the qualitative part of the data analysis method, using the theme analysis that was compiled with MAXQDA software and using the coding method, and in the quantitative part, the analysis method was based on Kendall's correlation test. According to the results of the research, 9 main themes, 20 sub-themes and 50 codes were discovered, which included the consequences of using artificial intelligence in international marketing. The research findings can have important results for exporters and international marketers

Abstract The emergence of Web 3, characterized by the integration of digital technologies such as artificial intelligence, machine learning, big data, and blockchain, has the potential to revolutionize traditional business practices. This transformative phase, referred to as Web 3 entrepreneurship, is disrupting established industries and creating new markets, thereby driving economic growth and generating employment opportunities. However, the rapid pace of technological advancement presents complex challenges that necessitate exceptional adaptability and continuous learning within the evolving entrepreneurial landscape. This study aims to identify the critical factors for growth and success, as well as to explore the implications of this modern form of entrepreneurship. To achieve this, a Delphi study was conducted involving 30 industry experts, leading to the identification of four categories of key success factors: technological, individual, environmental, and organizational. The findings of this research revealed that while there are potential adverse environmental, social, and economic consequences, such as the risk of creating a digital divide and increased energy consumption, there are also opportunities inherent in this technology. These opportunities include the potential to mitigate these consequences by fostering greater transparency and trust in transactions, creating new decentralized ecosystem-based opportunities, and developing sustainable solutions to address environmental challenges.