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August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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From text to insights: understanding museum consumer behavior through text mining TripAdvisor reviews

International Journal of Tourism Cities. 2023. Vol. 9. No. 3. P. 712–728.
Ivan Burkov, Aleksei Gorgadze

Purpose

This study aims to determine consumer satisfaction dimensions that lead to a willingness to share positive emotions through the study of TripAdvisor users’ reviews on St. Petersburg museums. The explorative study reveals the most significant factors that could predict museum visitors’ behavioral intentions.

Design/methodology/approach

The study is based on the theory of planned behavior and the “cognitive-affective-conative” model to analyze TripAdvisor reviews (n = 23020) and understand the relationship between the affective and the conative components of consumer behavior. Quantitative text-mining analysis allowed us to view every lemma of every review as a single factor for a deeper understanding of the phenomenon.

Findings

The research has enlarged the literature on museum consumer behavior. Behavioral intentions of museum visitors are affected by satisfaction dimensions, especially emotions felt; the esthetic dimension and museums’ surroundings affect consumers’ overall willingness to share positive emotions, while bad service quality and pricing policy make a visit to the museums less satisfying.

Practical implications

Managers can enhance their offerings and attract new consumers by identifying the satisfaction dimensions that influence their intentions to share positive emotions. The research findings can aid museums, tour agencies and government officials in developing targeted products and strategies to meet consumers’ expectations and promote urban tourism.

Originality/value

The research identified the dimensions that influence visitors’ willingness to share positive emotions through user-generated content in the context of museums. The study applies quantitative text analysis based on logit regression, which is a novel approach in the field of urban tourism research.

Research target: Economics and Management
Language: English
DOI
Text on another site
Keywords: user-generated contentmuseum consumer behaviorsatisfaction dimensions
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