2023. No. 2 Vol 17

      The purpose of this study is a conceptual description of the implementation of knowledge management systems (KMS) as a mechanism for universities’ strategic development. Knowledge management (KM) practice from around the world proved the positive influence of KMS on productivity of educational institutions. The theoretical provisions and concept for KMS are determined based on an analysis of international experience of KMS use in higher education (HE). Theoretical provisions consist of 1) the staff activities as an object of KM and knowledge because of these activities, 2) the specificity of HE restrains a transfer of the KM mechanism from business to HE, and 3) the uniqueness of each university determines the structure and content of KMS for strategic development. The KM process in HE is reflected in the Socialization-Externalization-Combination-Internalization (SECI) model, where each stage contains a list of staff activities and a set of digital services. The novelty of the KM process model in HE is that knowledge flows in a wave, not a spiral. In this motion, knowledge passes from uncodified to partly codified and codified form. The study demonstrates that knowledge can go the from stage of partly codified to uncodified for revision, and knowledge flow can stop at any stage. The advantage of the concept we designed is the ability to control the flow of knowledge before it takes the codified form of a document. The digital environment for KM first allows management to control faculty activities at the initial stage of uncodified knowledge through measurement of activities, and then to estimate the knowledge flow itself. The gathered indicators help to make decisions to motivate or restrain faculty. The university management gets a complete picture of faculty activities with knowledge and the intensity of knowledge flow in training courses and educational programs.

      This article deals with the problem of improving the effectiveness of a marketplace. The stakeholders of a marketplace are buyers and sellers. The objects are the aggregate of homogeneous products. The effectiveness of the trading platform, which can be characterized by the number of transactions made, will depend on how sufficiently the sellers put up offers. The paper looks at mathematical models to support the decision-making of the seller in making such offers. Focusing not only on the buyer demand but also on the presence of competitors on the site is a distinguishing feature of the models. To describe the competition, the apparatus of game theory is offered, namely the normal form of the game with a bimatrix model with two players: the seller – customer of service and the coalition of other sellers. To match offer and demand, as well as to find the probability of a transaction, fuzzy set theory and aggregation using the Choquet integral are used.

      Digital innovative products often become a significant factor in the revision of companies’ business strategies and influence consumer preferences. A key component in the process of formulating such strategies is understanding the implications underlying the attributes of digital products. This requires a good understanding of their nature and characteristics. To date, there is no solid basis for classifying various digital products according to their inherent characteristics. This paper presents a new interpretation of “digital products” based on the analysis of 2954 scientific articles from the Scopus database. It discusses the problems of differentiation of digital products from other types of products (such as “cyber-physical products,” “digitized products,” “smart products,” etc.). We also developed a new classification of digital products by the method of highlighting their key attributes. The purpose of the study is to develop an advanced classification of digital products based on their differentiation from other types of products. The classification we constructed based on the principles of differentiation will allow innovators and businessmen to create more profound and more advanced business models.

      Onlinу recruitment systems have accumulated a huge amount of data on the real labor market in recent years. Of particular interest to the study are the data on the real requirements of the labor market contained in the texts of online vacancies, as well as the process of extracting and structuring them for further analysis and use. The stage of compiling an up-to-date list of requirements for a position profile in the recruitment process is very time-consuming and requires a large amount of effort from an HR specialist related to monitoring changes in entire industries and professions, as well as analyzing relevance of existing requirements on the market. In this article, the author proposes a conceptual model of a recommendation system that allows one to reduce the burden on an HR specialist at the stage of forming an up-to-date list of requirements for a position profile in the recruitment process. The model is based on a combination of the following components: a graph model of labor market requirements based on the ESCO taxonomy adapted for the Russian language; and an intelligent method of forming recommendations for compiling an up-to-date list of requirements in the recruitment process based on neural network models of the language on the architecture of transformers, ESCO skills taxonomy and corpus online vacancies of the Russian labor market. The article also provides a conceptual algorithm for the work of the recommendation system and possible options for recommendations on updating the list of requirements of the position profile in the recruitment process based on an analysis of the needs of the real labor market.

      The contribution of the real estate industry to the global and regional economy is remarkable, yet in today’s evolving digital technology and digital economy, the digital transformation of the real estate industry is lagging behind other industries. This is, on the one hand, due to the solidified processes and systems linked to the upstream and downstream real estate industries, and, on the other hand, due to the fact that digital technology disrupts traditional ways of doing business, making the industry full of uncertainty. The digital transformation of the real estate industry is a broad and emerging concept. Various related research fields are concerned with the penetration and application of different innovative technologies to the industry. This study provides a systematic review focusing on the field of smart real estate using the bibliometric analysis approach under the guidance of PRISMA. The bibliometric analyses were performed in RStudio by utilizing 22 scientific documents indexed in Scopus and Web of Science that were published from 2012 to 2022. The findings suggest that: (i) smart real estate research is still a new but rapidly emerging field; (ii) only limited academic institutions from a few countries, such as the University of New South Wales in Australia, have shown significant contributions; (iii) the research exhibits specific collaborative network characteristics, leading to a high concentration of authors and citations; and (iv) data-driven topics such as “machine learning,” “information management,” “data analytics” and “big data” indicate a high degree of research density and centrality.