Publications

Purpose: The research aims to examine the impact of economic policy uncertainty (EPU) on a company's behaviour concerning its human capital. Additionally, the difference in effect for companies with specific human capital is analysed.

Design/methodology/approach: The hypotheses are tested on a multi-industry sample of large public companies from five European countries, using panel data modelling. The index of Baker et al. (2016) is used to measure EPU.

Findings: In the case of increasing EPU on one standard deviation, companies tend to reduce their human capital by approximately 1.7%. Moreover, despite theoretical assumptions, the effect on companies with more specific human capital is twice stronger. The heterogeneity of effect across countries and industries is also present.

Practical implications: Regulators and governments should consciously introduce changes in relation to regulations and decrease the uncertainty of economic policy to stimulate corporate investments in human capital.

Originality/value: This is the first study that considers the mechanism of EPU and its influence on corporate human capital. The results suggest that concerns regarding economic policy cause companies to reduce human capital.

The need for accurate balancing in electricity markets and a larger integration of renewable sources ofelectricity require accurate forecasts of electricity loads in residential buildings. In this paper, we considerthe problem of short-term (one-day ahead) forecasting of the electricity-load consumption in residentialbuildings. In order to generate such forecasts, historical electricity consumption data are used, presentedin the form of a time series with a fixed time step. Initially, we review standard forecasting methodologiesincluding naive persistence models, auto-regressive-based models (e.g., AR and SARIMA), and the tripleexponential smoothing Holt-Winters (HW) model. We then introduce three forecasting models, namelyi) the Persistence-based Auto-regressive (PAR) model, ii) the Seasonal Persistence-based Regressive (SPR)model, and iii) the Seasonal Persistence-based Neural Network (SPNN) model. Given that the accuracy ofa forecasting model may vary during the year, and the fact that models may differ with respect to theirtraining times, we also investigate different variations of ensemble models (i.e., mixtures of the previ-ously considered models) and adaptive model switching strategies. Finally, we demonstrate through sim-ulations the forecasting accuracy of all considered forecasting models validated on real-world datagenerated from four residential buildings. Through an extensive series of evaluation tests, it is shown thatthe proposed SPR forecasting model can attain approximately a 7% forecast error reduction over standardtechniques (e.g., SARIMA and HW). Furthermore, when models have not been sufficiently trained, ensem-ble models based on a weighted average forecaster can provide approximately a further 4% forecast errorreduction.

Transcranial magnetic stimulation is considered as a promising diagnostic and therapeutic approach, despite the fact that its mechanisms remain poorly understood. Theoretical models suggest that TMS-induced effects, within brain tissues, are rather local and strongly depend on the orientation of the stimulated nervous fibers. Using diffusion MRI, it is possible to estimate local orientation of the white matter fibers and to compute effects, that TMS impose at each point of them. The computed effects may be correlated with the experimentally observed TMS effects. However, since TMS effects are rather local, such relationships are likely to be observed only for a small subset of the reconstructed fibers. In this work, we present an approach for finding such a TMS-targeted subset of fibers, within a cortico-spinal tract, following stimulation of the motor cortex. Finding TMS-targeted groups of fibers is an important task for both (1) better understanding of the neuronal mechanisms, underlying the observed TMS effects and (2) development of future optimization strategies for TMS-based therapeutic approaches.

This book constitutes the proceedings of the 16th International Conference on Formal Concept Analysis, ICFCA 2021, held in Strasbourg, France, in June/July 2021.

The 14 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 32 submissions. The book also contains four invited contributions in full paper length.

The research part of this volume is divided in five different sections. First, "Theory" contains compiled works that discuss advances on theoretical aspects of FCA. Second, the section "Rules" consists of contributions devoted to implications and association rules. The third section "Methods and Applications" is composed of results that are concerned with new algorithms and their applications. "Exploration and Visualization" introduces different approaches to data exploration.

Business‐like approaches are applied more and more widely in nonprofit organization contexts, and theaters are no exception. Revenue generation, customer segmentation, and personalized marketing are becoming the key managerial concerns. Our study focuses on two relevant aspects of theater attendees' behavior. We examine visitors' willingness‐to‐pay (WTP) for theater seats (to derive revenue drivers), and its difference between two segments – single and couple visitors (to uncover the social motivation effect). These aspects taken together have never been previously studied in the nonprofit marketing context. We model WTP using the actual purchase data from Perm Opera and Ballet Theatre in Russia. Unlike most marketing studies which use stated preference for WTP evaluation, we employ the revealed preference approach. The results verify that single and couple visitors may be treated as separate segments, allowing for personalized promotion and other marketing decisions.

This research was aimed at analyzing the moderating role of region on the impact of internal and external sources of knowledge on product innovation from a multilevel perspective. This study has made a contribution to the knowledge and innovation management field for small and medium-sized enterprises (SMEs), by analyzing the utilization of internal and external sources of knowledge in rapidly changing environments, such as the Russian business context, with consideration given to regional disparity. Empirical estimations are carried out on the basis of more than 700 Russian manufacturing SMEs, observed in 2018 within the framework of the project, “Factors of Competitiveness and Growth of Russian Manufacturing Enterprises”. Internal and external sources of knowledge were identified through latent variables and a method of hierarchical linear modeling was applied, considering firm-level data nested within different regions. The results obtained, show that in Russian SMEs, when considering the moderation role of the region, internal and external knowledge have a positive impact on product innovation. Moreover, external knowledge contributes more by comparison to internal knowledge. Meanwhile, the region context conditions the strength of the innovation effect for both knowledge sources. The significance of regional conditions in transforming internal and external sources of knowledge into product innovation, requires specific policy elaboration at regional level. Moreover, the dominating role of external knowledge sources for product innovation in SMEs, proves the necessity of specific policy elaboration with regard to the knowledge-sharing infrastructure connecting different business units.

Background and Purpose Despite the continuing efforts in multimodal assessment of the motor system after stroke, conclusive findings on the complementarity of functional and structural metrics of the corticospinal tract (CST) integrity and the role of the contralesional hemisphere are still missing. The aim of this work was to find the best combination of the motor system parameters, allowing classification of patients into three predefined groups of upper limb motor recovery.

Methods 35 chronic ischemic stroke patients (47 [26–66] y.o., 29 [6–58] months post-stroke) with only supratentorial lesion and unilateral upper extremity weakness were enrolled. Patients were divided into three groups depending on the upper limb motor recovery. Non-parametric statistical tests and regression analysis were used to investigate the relationships among structural and functional motor system parameters, probed by diffusion tensor imaging (DTI) and transcranial magnetic stimulation (TMS). In addition, stratification rules were tested, using a decision tree classifier to identify parameters explaining motor recovery.

Results Fractional anisotropy (FA) ratio in the internal capsule (IC) and absence/presence of motor evoked potentials (MEPs), were equally discriminative of the worst motor outcome group (96% accuracy). MEP presence diverged for two investigated hand muscles. Concurrently, for the three recovery groups’ classification, the best parameter combination was: IC FA ratio and Fréchet distance between the contralesional and ipsilesional CST FA profiles (91% accuracy). No other metrics had any additional value for patients’ classification.

Conclusions This study demonstrates that IC FA ratio and MEPs absence are equally important markers for poor recovery. Importantly, we found that MEPs should be controlled in more than one hand muscle. Finally, we show that better separation between different motor recovery groups may be achieved when considering the whole CST FA profile.

The paper investigates the variety of peer effects on individual performance in a team sport. The individual performance of more than 5,000 soccer players, from 234 teams, between 2010 and 2015, is measured with the help of the FIFA video game simulator developed by EA Sports. The study reveals positive peer effects on individual performance although the marginal benefit decreases. Additionally, team cohesion contributes to an improvement of players’ ranking.

This paper examines people's willingness to separate collection of plastic waste. The study is based on a questionnaire survey of Perm residents and visitors, the data were analyzed using econometric methods. We identified key factors that determine the ecological behavior of people - the importance for a person of the benefits that he carries for the environment, the willingness to devote personal time to measures to protect it and awareness of the environmental harm of plastic waste. On average, all things being equal, an individual's awareness of the environmental hazards of plastic leads to increase the probability of his participation in the separate collection of plastic by 11.78%. On average, all things being equal, the proximity of containers for separate collection of plastic waste increases the probability that a person will participate in the separate collection of plastic by 45.57%. It should be noted that the probability of participation in a separate collection is lower for men than for women, all things being equal. Also, all things being equal, the probability of participation in the separate collection of plastic increases with age by 0.3%. 

In this paper, we rethink the corporate digital divide, a phenomenon not studied in detail in prior research. Motivated by innovation-diffusion, competence-based and skill-biased technical change theories, we hypothesize that all digital technologies’ innovations must be supported by demand for related skills and should be integrated into an innovation cycle. This research is conducted using a vast dataset of 1000 large Russian firms observed over ten years, with information collected from open internet-based sources and processed through content analysis. Among the key findings, the digital-innovation cycle has been explored and visualized, by identifying the most probable period of these innovations and their further diffusion. The digital-divide concept has been explicated by examining data on the relative dynamics of digital skills demanded by the same companies during the period of investigation. The empirical results deliver an interesting insight and encourage us to rethink the corporate digital divide through causality between competency accumulation and digital technological shifts. That, in turn, identifies the conditions necessary for the prediction of demand shocks in relation to digital competencies in labor markets.

Purpose

Focusing on managerial problems related to the measurement of intangibles, this paper develops and validates a hedonic-pricing methodology for the evaluation of the intangible resources of companies obtaining their shadow prices.

Design/methodology/approach

The paper adapts a hedonic-pricing methodology developed primarily for markets in real estate and secondhand cars to define how much intangibles may contribute to companies' market value. A certain calibration of the original tool has been developed to make this methodology appropriate for interpretation and practical use. The main advantage of this approach is that it allows for an evaluation of the shadow prices of intangible resources. These prices can be interpreted as the market value of the intangible resources which are not reflected on the balance sheet.

Findings

The results of this study demonstrate that hedonic pricing with a self-selection correction generates robust estimates. As one can see, the positive contribution of a high endowment of intangibles for all shadow prices is confirmed through estimations using two different techniques. Meanwhile, the negative effect of a low endowment is even more evident for the baseline model. This model shows consistent negative shadow prices for the majority of underinvested intangibles. Brands have the highest shadow prices in the introduced models; human capital, as measured by the qualification of top management and investments in employees, has likewise demonstrated high prices. However, most structural resources seem to be not reflected to a large degree in companies' market value.

Practical implications

This paper brings new opportunities to obtain the monetary value of intangible resources based on estimated market prices of a corporation's resource portfolio. These prices may be used for several purposes – for example, benchmarking for performance management, capital budgeting or knowledge-management practices. Moreover, by having methodological value, this study opens ways to evaluate any other intangibles which are not explicitly discussed in the empirical test of this particular study.

Originality/value

This study primarily contributes to the methodological advancement of evaluation of corporate intangible resources. It departs from the conventional hedonic-pricing mechanism to identify cogent estimates to intangibles in monetary terms. Importantly, this mechanism implies individual shadow prices for specific intangible resources which makes the contribution of this study unique for the existing literature, both within resource-based and value-based views.

Research on individual differences in the fields of chronobiology and chronopsychology mostly focuses on two – morning and evening – chronotypes. However, recent developments in these fields pointed at a possibility to extend chronotypology beyond just two chronotypes. We examined this possibility by implementing the Single- Item Chronotyping (SIC) as a method for self-identification of chronotype among six simple chart options il- lustrating the daily change in alertness level. Of 2283 survey participants, 2176 (95%) chose one of these op- tions. Only 13% vs. 24% chose morning vs. evening type (a fall vs. a rise of alertness from morning to evening), while the majority of participants chose four other types (with a peak vs. a dip of alertness in the afternoon and with permanently high vs. low alertness levels throughout the day, 15% vs. 18% and 9% vs. 16%, respectively). The same 6 patterns of diurnal variation in sleepiness were yielded by principal component analysis of sleepiness curves. Six chronotypes were also validated against the assessments of sleep timing, excessive daytime sleepi- ness, and abilities to wake or sleep on demand at different times of the day. We concluded that the study results supported the feasibility of classification with the 6 options provided by the SIC.

This article contributes to the development of contestable market theory by investigating how competitiveness in the eSports industry influences the size of this industry, as measured by the volume of monetary prizes. We use data on each gamer's prize earnings for each tournament from 1999 to 2015 to estimate panel vector autoregression (V.A.R.) model with fixed effects. The main finding is that competition does not increase industry size. This result confirms the hypothesis from the contestable market theory that perfect competition does not always facilitate better development, especially in industries where natural barriers result in a small number of leading firms or teams.

In the last thirty years, a significant shift from the homology to omnivore argument has occurred in musical preference studies. Studies on the omnivore argument mainly come from North and South America, Western and sometimes Eastern Europe. To the best of our knowledge, there are no empirical tests of musical omnivorousness in Russia. The aim of this paper is to reveal omnivore musical preferences in Russia, and analyzes the links between musical preferences, social-demographic profiles, and tolerance. Our research also emphasizes the territory dimension. The research setting is the Perm Region. A survey of 2,400 Perm Region citizens is analyzed using principal component analysis and linear regression provides evidence for the research. Our findings do not indicate omnivore musical tastes in Russia that contradicts the conclusions of the research in other cultures. Instead of finding the omnivore pattern, we found  Bourdieu-like patterns of classical versus pop music taste and nostalgic taste versus contemporary taste. Representatives of each taste pattern have a specific social-demographic profile. The urbanization factor influences musical preferences as well. The paper discusses the limitations of the research and directions for further work.

In the modern scientific literature, there are many reports about the successful application of neural network technologies for solving complex applied problems, in particular, for modeling the urban real estate market. There are neural network models that can perform mass assessment of real estate objects taking into account their construction and operational characteristics. However, these models are static because they do not take into account the changing economic situation over time. Therefore, they quickly become outdated and need frequent updates. In addition, if they are designed for a specific city, they are not suitable for other cities. On the other hand, there are several dynamic models taking into account the overall state of the economy and designed to predict and study the overall price situation in real estate markets. Such dynamic models are not intended for mass real estate appraisals. The aim of this article is to develop a methodology and create a complex model that has the properties of both static and dynamic models. Moreover, our comprehensive model should be suitable for evaluating real estate in many cities at once. This aim is achieved since our model is based on a neural network trained on examples considering both construction and operational characteristics, as well as geographical and environmental characteristics, along with time-changing macroeconomic parameters that describe the economic state of a specific region, country, and the world. A set of examples for training and testing the neural network was formed on the basis of statistical data of real estate markets in a number of Russian cities for the period from 2006 to 2020. Thus, many examples included the data relating to the periods of the economic calm for Russia, along with the periods of crisis, recovery, and growth of the Russian and global economy. Due to this, the model remains relevant with the changes of the international economic situation and it takes into account the specifics of regions. The model proved to be suitable for solving the following tasks: industrial economic analysis, company strategic and operational management, analytical and consulting support of investment and construction activities of professional market participants. The model can also be used by government agencies authorized to conduct public cadastral assessment for calculating property taxes.

Information about companies published in a news feed is invariably tinted by emotional tonality. As such, resulting perceptions may influence the opinion of market players, and consequently affect the dynamics of a company’s share price. This study aims to evaluate various hypotheses about the impact of the tone of news items regarding dividends, capital expenditures, and development on the stock prices of Russian companies. Information disclosure is extensively studied, and there have been limited studies on the effect of disclosures on Russian companies. However, until now, there have been no research studies which verify hypotheses on the influence of news sentiment on corporate share prices in the Russian market.

This analysis was conducted using data from 49 Russian public companies included in the Moscow exchange index over the period from the end of 2017 to the beginning of 2019. To account for the proximate impact of news items on consequential market phenomena, an event study methodology was applied in order to estimate and construct the models of dependency of cumulative abnormal return (CAR) on news tone level, and control for financial and nonfinancial factors.

Our results provide evidence for the positive impact of the tone of news texts on the share prices of Russian companies. The increase in news tone by one standard deviation leads to a cumulative abnormal stock return increase of 0.26 percentage points. This result is consistent with previous research conducted on data from developed stock markets. Moreover, the relationship between the tone or sentiment level of a news item and the stock price reaction is linear, without the diminishing marginal effect.

Our conclusions should prompt companies to invest effort in delivering information in a tonally positive way, highlighting the most positive news. Investors, in turn, should rationally approach the interpretation of published information.

Using the assignment of referees to European international association football matches played between 2002 and 2016, we ask whether judgements were biased according to the legacy of the Cold War. Referees from post-communist states favoured teams from non-communist states, but there was no evidence of favouritism in the other direction. The out-group bias of referees born behind the Iron Curtain was more significant for less important decisions (e.g., yellow cards vs red cards). The bias was particularly large among referees from the former Soviet Union. It has also diminished over time, perhaps due to increased professionalism in European refereeing, or because memories of the Cold War era have diminished among active referees.

In this study, we estimate an attendance demand model in a reduced form, with uncertainty as one of the determinants of demand, to test the uncertainty of outcome hypothesis (UOH). Data from the Russian Football Premier League (RFPL) are used. These data fit our requirements for two reasons. First, there are few sellout matches, so demand for tickets in the RFPL is not restricted by stadium capacity. Secondly, there have been no articles devoted to the study of outcome uncertainty in the RFPL. The results indicate that the UOH does not explain the behavioral pattern of attendees in the RFPL. The dependence between attendance and uncertainty is U-shaped. We observe some evidence that attendee’s utility in the RFPL depends more on seeing a home team win.

The article presents analytical review of existed solutions and technologies applied in computer vision control access systems, video monitoring and analysis areas. Such technologies are parts of the smart city concept and commonly used for recognition of faces in modern office buildings and business centers. Face recognition is used to distinct employees and guests, separated rooms and to evaluate the position of people, to expose atypical behavior. Commercial centers use such technologies for storing marketing information and estimating more popular route of buyers’ movements. The article discusses the process of construction and realization of object recognition in video stream prototype system. This prototype uses single-board computer Raspberry Pi 3 model B+ and RPi-camera (Raspberry Pi Camera Board v2.1). Prototype can be used as the video recording module of “Smart office” or “Smart home” system.

The imbalance between the rapidly growing rates of motorization and the unsuccessful development of the transport network leads to the destruction of transport network facilities. On the roads, this problem is solved by updating the coverage. The application of this technology to artificial structures (bridges, overpasses, overpasses) is ineffective.

To preserve artificial structures in a standard condition, it involves the introduction of restrictions on the weight and dimensions of vehicles.

This paper proposes the development of a unified registration automated complex for monitoring vehicles in regular traffic.

The advantages of the proposed automated complex are determined by the simplicity of its use, the formation of a single database collected from existing traffic management systems in real time, automatic processing of the received data and instantaneous measures to prevent violations of the established restrictions.

The approbation of this automated complex was carried out when simulating the situation on the section of the regional road of the first technical category 57K-0002 "Perm - Berezniki" km 22 - km 24 on an artificial structure - a bridge across the Chusovaya river.

The effectiveness of the automated complex on an artificial structure - a bridge across the Chusovaya River is confirmed by the fact that after the start of its operation, the transport and logistics characteristics of this artificial structure have significantly improved.

This article is devoted to an empirical analysis of key stakeholders’ values in the context of sustainable development concept. The study was conducted on the example of the regions of the Volga and Ural federal districts. The methodological research framework is the concept of sustainable development and a value-driven approach to the management of economic systems, which allows to analyze the main economic agents and determine interests realization level. The information base is statistical data and reference materials provided by federal agencies, regulatory legal acts, author's developments. Relationships strength assessment is based on econometric modeling. Developed models include endogenous and exogenous values indica-tors. Research results indicate the interdependence of the groups of the population, the bus-ness community and public authorities’ values. Moreover, a significant influence on the values implementation degree by the institutional environment conditions is found. Fixed effects assessment provides the dependence of key stakeholders’ values realization level in the region on shocks in different time periods.