Special Issue "Digital Transformation: Instrumental and Mathematical Methods and Models"

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 4239

Special Issue Editor

Dr. Dmitry M. Nazarov
E-Mail Website
Guest Editor
Department of Business Informatics, Ural State University of Economics, 620144 Yekaterinburg, Russia
Interests: digital economy; fuzzy model; big data; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital transformation is a process in which the improvement and improvement of business processes are based on the intelligent processing of the data of these business processes. This, ultimately, allows you to "copy" the business processes of organizations in order to standardize and optimize them. The business process in any organization should be considered by researchers in a comprehensive manner, taking into account all aspects of the activity: technical, socio-economic and legal. Currently, there is a discrepancy between the theory and practice of studying and implementing complex models of digital transformation in the activities of organizations on a global scale.

The aim of this Special Issue is to publish scientific articles on the development and implementation of advanced mathematical and instrumental methods in the digital transformation of the economy, based on Big Data technologies, Data Mining, Internet of Things and econometric assessments.

Thus, our release is designed to reduce the mismatch between theory and practice of digital transformation.

Dr. Dmitry M. Nazarov
Guest Editor

Manuscript Submission Information

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Keywords

  • digital economy
  • fuzzy model
  • big data
  • data mining: cluster analysis, neural networks, recommender systems
  • internet of things
  • digital transformation
  • econometric methods
  • financial and actuarial calculations

Published Papers (4 papers)

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Research

Article
The Model of OTC Securities Market Transformation in the Context of Asset Tokenization
Mathematics 2022, 10(19), 3441; https://doi.org/10.3390/math10193441 - 22 Sep 2022
Viewed by 724
Abstract
The relevance of this study stems from the fact that the development of a market for financial instruments can significantly expand lending opportunities for small- and medium-sized businesses. While research on the impact of tokenization on financial markets is extensive, literature provides virtually [...] Read more.
The relevance of this study stems from the fact that the development of a market for financial instruments can significantly expand lending opportunities for small- and medium-sized businesses. While research on the impact of tokenization on financial markets is extensive, literature provides virtually no description of mathematical models that can be used in the design and development of information systems issuing tokenized financial instruments. Thus, the study aims to develop mathematical models representing the transformation of the over-the-counter (OTC) securities market induced by the tokenization of underlying assets. The development of crowdlending platforms is gradually transforming the financial market landscape. The key change trends consist in transactional fragmentation both on the demand and supply sides. This paper proposes a mathematical model of internal transformation occurring in the OTC financial market, which describes the process of managing rights to underlying assets during their issuance and circulation. The model is built by analogy with the Harrison–Ruzzo–Ullman (HRU) model, applying the same principles to the relations of economic agents in exercising access rights to underlying assets as those that regulate access rights to files. The research novelty of the presented model consists in the formalization of financial market transformation occurring in the context of asset tokenization, which significantly expands the mathematical apparatus of digital financial transactions. This paper also proposes a mathematical model of competitive tokenization-induced transformation occurring in the OTC financial market, which describes transaction costs associated with attracting investment in the OTC financial market and the market for tokenized assets. In addition, the barriers of the OTC financial market and the stock market are described indicating the supply and demand trends in the context of transformation occurring in the OTC financial market under the influence of underlying asset tokenization. The novelty of this model lies in the mathematical formalization of the investment attraction process in the market for tokenized assets. The theoretical value of the developed models consists in the confirmation of significantly expanded supply capabilities of tokenized assets on the graph showing the dependence of asset returns on invested capital. Full article
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Article
Clustering of Dark Patterns in the User Interfaces of Websites and Online Trading Portals (E-Commerce)
Mathematics 2022, 10(18), 3219; https://doi.org/10.3390/math10183219 - 06 Sep 2022
Viewed by 1277
Abstract
dark patterns in the interfaces of users using sites and portals of online trading affect their behavior by companies that own digital resources. The authors propose to implement the detection of dark patterns on sites in user interfaces using cluster analysis algorithms using [...] Read more.
dark patterns in the interfaces of users using sites and portals of online trading affect their behavior by companies that own digital resources. The authors propose to implement the detection of dark patterns on sites in user interfaces using cluster analysis algorithms using two methods for clustering many dark patterns in application interfaces: hierarchical and k-means. The complexity of the implementation lies in the lack of datasets that formalize dark patterns in user interfaces. The authors conducted a study and identified signs of dark patterns based on the use of Nelsen’s antisymmetric principles. The article proposes a technique for assessing dark patterns using linguistic variables and their further interval numerical assessment for implementing cluster data analysis. The last part of the article contains an analysis of two clustering algorithms and an analysis of the methods and procedures for applying them to clustering data according to previously selected features in the RStudio environment. We also gave a characteristic for each resulting cluster. Full article
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Article
Pulse of the Nation: Observable Subjective Well-Being in Russia Inferred from Social Network Odnoklassniki
Mathematics 2022, 10(16), 2947; https://doi.org/10.3390/math10162947 - 15 Aug 2022
Cited by 2 | Viewed by 914
Abstract
Policymakers and researchers worldwide are interested in measuring the subjective well-being (SWB) of populations. In recent years, new approaches to measuring SWB have begun to appear, using digital traces as the main source of information, and show potential to overcome the shortcomings of [...] Read more.
Policymakers and researchers worldwide are interested in measuring the subjective well-being (SWB) of populations. In recent years, new approaches to measuring SWB have begun to appear, using digital traces as the main source of information, and show potential to overcome the shortcomings of traditional survey-based methods. In this paper, we propose the formal model for calculation of observable subjective well-being (OSWB) indicator based on posts from a social network, which utilizes demographic information and post-stratification techniques to make the data sample representative by selected characteristics of the general population. We applied the model on the data from Odnoklassniki, one of the largest social networks in Russia, and obtained an OSWB indicator representative of the population of Russia by age and gender. For sentiment analysis, we fine-tuned several language models on RuSentiment and achieved state-of-the-art results. The calculated OSWB indicator demonstrated moderate to strong Pearson’s (r=0.733, p=0.007, n=12) correlation and strong Spearman’s (rs=0.825, p=0.001, n=12) correlation with a traditional survey-based Happiness Index reported by Russia Public Opinion Research Center, confirming the validity of the proposed approach. Additionally, we explored circadian (24 h) and circaseptan (7 day) patterns, and report several interesting findings for the population of Russia. Firstly, daily variations were clearly observed: the morning had the lowest level of happiness, and the late evening had the highest. Secondly, weekly patterns were clearly observed as well, with weekends being happier than weekdays. The lowest level of happiness occurs in the first three weekdays, and starting on Thursday, it rises and peaks during the weekend. Lastly, demographic groups showed different levels of happiness on a daily, weekly, and monthly basis, which confirms the importance of post-stratification by age group and gender in OSWB studies based on digital traces. Full article
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Article
A Model for Assessing the Causality of Factors in the Development of Voluntary Pension Insurance in the Republic of Kazakhstan
Mathematics 2022, 10(9), 1415; https://doi.org/10.3390/math10091415 - 22 Apr 2022
Viewed by 779
Abstract
Many countries have been experiencing a crisis in their pension systems for fiscal and demographic reasons. Voluntary pension funds are a way out of the crisis. The depth of the problem lies in the study of social and economic-mathematical aspects in making economic [...] Read more.
Many countries have been experiencing a crisis in their pension systems for fiscal and demographic reasons. Voluntary pension funds are a way out of the crisis. The depth of the problem lies in the study of social and economic-mathematical aspects in making economic decisions on implementing voluntary contributions. The authors studied sustainable development, considering the assessment of the causal relationship between factors in the development of voluntary pension insurance in the Republic of Kazakhstan. The article analyzes pension system models and studies the experience of the OECD countries. The results of the analysis highlight the most important factors affecting the development of pension systems with an emphasis on voluntary pension insurance mechanisms. The authors propose a conservative, economic, extended economic, and extended intermediate solution for building a set of cause-and-effect models for the development of voluntary pension insurance in the Republic of Kazakhstan based on a survey of a representative sample of citizens in the Republic of Kazakhstan using the QCA method. Full article
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