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June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
June 3, 2026
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
June 3, 2026
Creative Work as a Remedy for Burnout
The creative, supportive atmosphere and innovative methods at the Centre for Sociocultural Research make it appealing to early-career scholars. Over years of working at HSE University, they grow into researchers and lecturers recognised both in Russia and abroad. Chief Research Fellow Zarina Lepshokova and Leading Research Fellow Ekaterina Bushina spoke about their journey at the centre and at HSE, their research, and the role of mentors in their academic success.

 

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A maturity model for digital information management in university libraries: a design science study

International Information and Library Review. 2022. P. 1–25.
Keshavarz H.
In press

Identifying dimensions and levels of maturity in digital information management is of high significance for university libraries because it would highly influence their daily businesses and future decisions. The current study is an effort to develop, confirm and test a maturity model for digital information management in the context of university libraries as a response to ever-increasing digital information. A design science approach in qualitative-quantitative order was considered as the research design. Initially, a meta-synthesis was conducted to systematically review the related literature and to develop the conceptual model for dimensions and levels. Afterward, a set of 30 related experts analyzed and confirmed the model with a 95% confidence level. Finally, the maturity levels of 14 central university libraries across Iran were measured through a multiple case study design. The MMDIM (Maturity Model for Digital Information Management) includes 10 dimensions, 20 components and 5 levels. Regarding priorities given to the components by the experts, the digital business model and digital work environment were the first and last priorities respectively. The results showed that most selected libraries were at level three namely slight management of digital information. Moreover, library managers evaluated levels of their maturity which showed that digital processes and operations and digital culture were given the first and last levels respectively. The model could be regarded as a diagnostic tool to identify development needs that could be supported by objectives of other similar libraries.

Research target: Media and Communications
Language: English
DOI
Keywords: maturity modelsLibrariesdigital information
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