Valeriia Arteeva

Mrs. Valeriia Arteeva

Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Sankt-Peterburg, Russia

Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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27 December 2022 in International Journal of Technology
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Journal Article
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VAValeriia Arteeva
Valeriia Arteeva
ISIlya Sokol
Ilya Sokol
EAElmaz Asanova
Elmaz Asanova
DUDenis Ushakov
Denis Ushakov
The tourism industry is a powerful tool for developing the country's economy, as it contributes to creating new jobs and capital inflow. Tourism development is closely linked to the development of infrastructure, as well as to the rapid introduction of information and communication technologies (ICT) and extensive digitalization. The authors assessed the impact of digitalization factors and infrastructure development on domestic tourism in Russia. Two final models were described as a result of the econometric analysis. The number of tourists in the region was chosen as a dependent variable, and the infrastructure development index (IDI) /transport development index (TDI), the costs of implementing and using digital technologies, and the number of hotels proved to be significant explanatory. Our analysis shows a 1% increase in the ?osts of introduction and use of digital technologies in the first model corresponds to the rise in the number of tourists in the region by 0.15%, and by 0.13% in the second model. The results indicate that 1 unit rise in the TDI leads to an increase in tourists by 20.2%, and the IDI growth by 1 unit implies to an expansion of tourist flow by 70.5%, which is due to a reasonably low spread of these indices. Thus, the development of domestic tourism is closely related to the spread of digital technologies in the regions and infrastructure development.

1 January 2024 in Journal of Applied Economic Research
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Journal Article
Closed access
VAValeriia Arteeva
Valeriia Arteeva
ASAngi Erastievich Skhvediani
Angi Erastievich Skhvediani
SSSergey Sosnovskikh
Sergey Sosnovskikh
There is an escalating demand for highly skilled professionals in digital analysis in Russia heightened by the COVID-19 pandemic and the onset of the special military operation in 2022. This study aims to identify the precise competencies that employers seek for big data analytics (BDA) professions with the focus on data analyst (DA) and business analyst (BA). It also aims to examine the dynamics and evolving skillset structures of these two roles. Our sample size comprises 2,357 vacancies that were analysed in 2020 and 2023. Our multimethod approach involves four stages: data collection of job postings, data processing, identification of the skills structures, and statistical analysis and data visualisation. We also used various techniques such as web-scrapping, data parsing, tokenisation, n-gram extraction, and social network analysis. Our results indicate a shift in Russia, where DAs require to have a solid understanding of business concepts, familiarity with non-STEM fields, and soft skills such as management, communication, and teamwork. BAs must possess technical skills related to BDA, including tool use, programming, and data analytics. The emphasis on interpersonal skills, like creativity and empathy, is crucial for effective collaboration in the interdisciplinary BDA field. This research clarifies the specific competencies required for DA and BA roles, emphasising their interdisciplinary nature in the Russian context. It offers practical insights for educational institutions, organizations, and policymakers to align curricula, training, and policies with market demands, and provides guidance for job seekers to enhance their skills and employability.

29 April 2021 in Economic Analysis: Theory and Practice
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Journal Article
Closed access
VAValeriia Arteeva
Valeriia Arteeva
ASAngi Erastievich Skhvediani
Angi Erastievich Skhvediani
Subject. The article assesses the effectiveness of investments in higher education. Objectives. The aim is to assess the performance of investments in higher education for a Master’s student at the Peter the Great St. Petersburg Polytechnic University, in the field of Economics, Business Analyst Specialty. Methods. The methodology, presented in the study, includes three stages. The first assesses the demand for skills, the second assesses how the supply of skills match the demand, and the third – the effectiveness of investments in higher education, based on the developed mathematical model, scenario analysis, and decision tree. Results. We revealed that for a business analyst, the most important categories of skills are project management, decision-making, organizational competencies, communication, and knowledge of corporate software. The most required skills in these categories are the knowledge of business processes, project documentation, systems thinking, teamwork, communication, and well-bred speech. The analysis of correspondence between the competencies required by employers and those acquired in the training process showed that Master’s graduates meet the demand for the position of a business analyst in the labor market by 69%. Conclusions. The evaluation of the effectiveness of investment in higher education for a Master’s student of the Peter the Great St. Petersburg Polytechnic University, in the field of Economics, Business Analyst Specialty, shows that it is more profitable for a Bachelor graduate to continue studying for a Master's degree, rather than go straight to work.


16 June 2023
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Book Chapter
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MAMaria Anisimova
Maria Anisimova
IRIrina Andreevna Rudskaya
Irina Andreevna Rudskaya
ASAngi Erastievich Skhvediani
Angi Erastievich Skhvediani
VAValeriia Arteeva
Valeriia Arteeva

2 October 2024 in E3S Web of Conferences
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Conference Paper
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PDF file
VAValeriia Arteeva
Valeriia Arteeva
EMEleonora Markaryan
Eleonora Markaryan
SDSamandar Doliev
Samandar Doliev
ASAzizbek Shomirov
Azizbek Shomirov
The relevance of this study is due to the growing attention to the development of green economy and its potential impact on employment. The purpose is to evaluate how green economic development affects employment in Russia, using panel data from 82 regions spanning the period from 2016 to 2020. To analyze the data, we use generalized linear regression models such as OLS, random effects, and fixed effects models. The final fixed effects model explains about 25% of the total variation in unemployment rates across different regions. However, evidence of cross-sectional dependence in the residuals suggests potential challenges, such as the omission of relevant variables that capture unobserved heterogeneity and inadequate consideration of spatial clustering within specific regions. The main results show that expenditure on environmental protection and maintenance of protected areas are positively linked with unemployment. This suggests that these expenditures may not directly lead to job creation. There could be several reasons for that: administrative costs, the delayed effect of environmental investments, or regional disparities in infrastructure and skilled labor availability.