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September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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Ценностное предложение ресторанного бизнеса: интеллектуальный анализ онлайн-отзывов клиентов

Вестник Санкт-Петербургского университета. Серия 8. Менеджмент. 2022. Т. 21. № 1. С. 48–75.
Serova E., Fainshtein E.

The purpose of the paper is to study value proposition factors in online customer reviews that
characterize consumer inquiries in the catering industry by using text-mining techniques. Research
design focuses on the analysis of chain restaurants, which focuses on the usefulness of the
service indicated in a quantitative system of scores, and pleasure factors for customers. Empirical
data from 201 online reviews were collected from TripAdvisor service, which contains information
about consumer preferences from St. Petersburg, Russia. In addition to text mining, the
study used a correlation analysis of rating scores for chain restaurant customers, comparing the
online reviews of satisfied and dissatisfied customers. The results of the study identified certain
categories of factors of high importance for customers, which were revealed in both positive
and negative online reviews. The results of the analysis help the restaurant management system
to manage the value proposition properly taking into account various consumer factors of thepreferences indicated in reviews. The research implications allow managers to purposefully select
those factors that will achieve the desired effect in the relationship marketing. This study gives
an opportunity to management specialists of the company explore and account for the emotional
sentiment of online reviews.

Research target: Economics and Management Mathematics Computer Science
Language: Russian
Full text
DOI
Text on another site
Keywords: семантический анализкорреляционный анализtext miningsemantic analysis correlation analysisценностное предложениеchain restaurantsинтеллектуальный анализ текста (text mining)сетевые рестораныvalue proposition.online customer reviewsонлайн-отзывы клиентов
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