Business Informatics

This article presents an approach to simulation of the rates of road traffic accidents involving unmanned ground vehicles within a multi-agent intelligent transportation system for the ‘smart city.’ A new simulation model of an intelligent transport system has been developed which makes it possible to significantly reduce the number of potential road traffic accidents (TAs) and implements the concept of socially-centered management of the urban economy. The software implementation of such a large-scale agent-based model was carried out using the FLAME GPU framework, which allows us to effectively parallelize the agents’ behaviour logic and consider their individual decision-making systems when modelling the spacial dynamics of an ensemble of unmanned ground vehicles (UGVs) interacting with other road users: the usual manned ground vehicles (MGVs), unexpected obstacles (e.g., pedestrians, etc.). Various scenarios of such agents’ behaviour in an intelligent transportation system are studied, including the occurrence of an accident under certain conditions (e.g., under the high speed and traffic intensity of UGVs, etc.) and various configurations of the digital road network (DRN). We determine the parameter values that provide for the individual decision-making system of UGVs remaining stable with respect to the characteristics of the external environment (including in extreme situations), ensuring the safety of other road users on the scale of the ‘smart city.’

Andranik Akopov

Levon Beklaryan

Simulation of rates of traffic accidents involving unmanned ground vehicles within a transportation system for the ‘smart city’

“智慧城市”交通系统中无人驾驶地面车辆事故率模拟

In the period 2020-2022, the Russian economy has been facing the new, unprecedented challenges of coronavirus and sanctions. In order to analyze the current state of affairs, we are offering an econometric study of Russia’s macroeconomic production function for 1990-2022 and an estimation of the marginal rate of technical substitution under internal and external restrictions associated with the spread of the Wuhan coronavirus (SARS-CoV-2) and the conduct of Russia’s special military operation in Ukraine, accompanied by increased sanctions pressure on the Russian economy. We have obtained several significant results. In the years 1991-1996 the marginal rate of technical substitution was increasing, and in 1997-2020 it was decreasing except for 2008-2009 and 2015. In the context of the Wuhan coronavirus pandemic, the main reasons for the Russian economy’s decline in 2020 and growth in 2021 were, first of all, fluctuations in the world crude oil price, and not the Wuhan coronavirus pandemic as such. We did not find any evidence that the decline in the world crude oil price in 2020 was caused by a decrease in demand from China, since Russian oil exports to China increased. Contrary to many negative forecasts, the results of our forecasting of Russia’s GDP for 2022 show that under sharply increased sanctions pressure, with the world price of Urals oil at $60 per barrel, the average growth rate will be 0%, while at $70 it will be 4%, and at $80 it will be 7%. Under the reduced demand for Russian gas and the shutdown of the Nord Stream 1 gas pipeline, the forecast volumes of gross natural gas production by Gazprom (excluding Gazprom Neft) in the Tyumen Region for 2022, based on the exponential production function studied by econometric methods, range from 364 to 392 billion cubic meters. Using the example of Great Britain, where in 2021 the average actual export prices for Russian oil and gas were the lowest compared to other Western European countries, we discuss the economic inexpediency of setting marginal prices for Russian energy products by Western consumers.

Аnton Afanasiev

Olga Ponomareva

Macroeconomic production function of Russia and estimation of the marginal rate of technical substitution in the unprecedented socio-economic realities of 2020–2022

俄罗斯的宏观经济生产函数及其在不寻常的2020-2022年社会经济现实中的边际技术替代率估计

It is common knowledge that information and communication technologies (ICTs) have made continuous inroads in the knowledge management field; thus, this study is modeled to examine the impact of ICTs on inter-organizational knowledge sharing (IOKS) and its subsequent effect on the growth of small and medium-sized enterprises (SMEs). The study adopts a descriptive survey design, using the quantitative research approach. Using the simple random sampling technique, a web-based questionnaire was used to collect data from 187 respondents. Results showed that IOKS among SMEs is not carried out to a great extent, which means that it is not a common practice among SMEs. Findings showed that less than half of the SMEs used training programs, internship programs, research collaboration and workshops for IOKS. It further showed that IOKS enhances sales, productivity, profit, organizational assets and equity. This study provides evidence of how ICT systems/tools have been used in IOKS and their impact on the growth of SMEs.

Naief Azyabi

The impact of ICT on inter-organizational knowledge sharing for SMEs growth

ICT对中小企业成长中组织间知识共享的影响

This paper compares the driving factors of changes in energy intensity in both net energy exporting and importing countries using a DEA-Malmquist (Data Envelopment Analysis) and panel GMM (Generalized Method of Moments) methods over the period of 2000–2021. The findings show that technological progress has played a significant role in reducing of energy intensity in both groups. Moreover, we use the DEA method to decompose the Malmquist total factor productivity (TFP) into its components including technical change (TC), efficiency change (EC), pure efficiency change (PEC) and scale efficiency change (SEC). The results show that in energy exporting countries, the effects of each of these TFP components on energy intensity are negative but relatively weak, while the effects of these components on reducing energy intensity in importing countries is considerable. Specifically, the estimated coefficient of the pure efficiency component in reducing energy intensity in very remarkable, which shows the high importance of the efficiency components of TFP in energy management. Next, we investigate what is the main driver of technological progress in both the energy exporting and importing countries. The findings imply that in net energy exporting countries trade openness is a dominant factor to improve productivity, while in net energy importing countries, internal R&amp;D is the dominant factor for improving technological efficiency.

Mehdi Fallah Jelodar

Somaye Sadeghi

Driving factors of changes in energy intensity: A comparison between energy exporting and importing countries

能源强度变化的驱动因素：能源出口国与进口国的比较

The situation of a trade war between Russia and Western countries is unprecedented in recent history, both in terms of the scale of the restrictions being introduced and because of their mutually dangerous nature, as a result of which the entire world economic system is experiencing difficulties. An urgent task is to develop an economic policy for Russia that will allow for a quick reorientation to Eastern markets and the use of new growth drivers. Evaluation of the effectiveness of the measures taken should be carried out using modern tools, one of which is agent-based economic models. Since Russia is not considered as a key player in the models of international trade relations developed in a number of countries, in order to assess the sanctions imposed against it, it was necessary to develop a new tool – an agent-based model of trade wars between Russia, the USA, China and the European Union. The purpose of the study presented in this article is to assess the need of the Russian economy for additional investments in various industries for large-scale import substitution of products till now supplied from unfriendly countries. To achieve this, the agent-based model reproduces the sectoral structure of the considered economies of the countries and trade relations among them that existed before the start of the special military operation, compiles scenarios of possible sanctions, and simulates the corresponding changes in international trade relations. As part of the scenario calculations, three series of experiments were carried out. In the first series, for each scenario the expected dynamics of Russia’s GDP in 2022 was estimated in the context of organizing import substitution programs in key industries, and the cost of these programs was calculated. In the second series, the dependence of GDP dynamics on the volume of investments was studied. The third series simulated the dynamics of trade relations for the period up to 2025 for two investment policy options in each scenario. The results of the experiments also show that the impact of investments on the economy is stronger, the more severe the sanctions are, and under these conditions, the implementation of investment programs can accelerate economic recovery on average by 0.5% of GDP per year.

Alexandra Mashkova

Dynamics of investments in Russia under the conditions of sanction restrictions: Forecast based on an agent-based model

俄罗斯制裁限制下的投资动态：基于代理模型的预测

The necessity to enhance the efficiency of modern network enterprises based on digital platform technologies, Digital Twins, and Digital Threads determines the relevance of implementing dynamic multi-agent technologies in production practice. The architectural complexity of existing multi-agent systems (MAS) and the lack of scientific research in the field of justifying methods and tools for their creation motivate the goal of this study to develop a comprehensive MAS design technology. This technology should encompass all architectural levels and allow for the adaptation of reference and best design practices. This article analyzes the possibilities of applying Digital Twins and Digital Threads in the creation of network enterprises and proposes methods for their implementation using MAS. A design technology for MAS has been developed in accordance with the IIRA (Industrial Internet Reference Architecture) and RAMI (Reference Architectural Model Industrie 4.0) architectural frameworks, which enables the interconnected formation and display of design results across various architectural levels. At the business level, a method is proposed for formulating business requirements for MAS based on the selection and adaptation of business models and application scenarios. At the level of constructing manufacturing and business processes, a method for formulating functional requirements for MAS is presented, revealing the transition from value networks to manufacturing and business process structures. At the level of functional design of the network enterprise’s multi-agent system, a method is proposed for forming key design solutions from the perspective of implementing various service categories using AAS (Asset Administrative Shells) and their specialization. At the technological implementation design level of MAS, a method for implementing software agents using a microservice software organization is proposed. The method presented for adapting reference and best MAS design models allows for the selection of appropriate design solutions from libraries of reference models and knowledge bases for subsequent refinement. This accelerates and improves the quality of the design process. The implementation of the developed technology for designing multi-agent systems will increase the adaptability of network enterprises to dynamically changing business needs, taking into account the interests and capabilities of all stakeholders.

Yury Telnov

Vasiliy Kazakov

Andrey Danilov

Designing a multi-agent system for a network enterprise

网络企业多代理系统设计

Purchases made on online platforms have heavily incorporated artificial intelligence (AI) to shape consumer purchasing behavior. To investigate re-purchase intentions, this study combines AI, social media engagement, conversion rate optimization, brand experience and brand preference. A survey was conducted with a questionnaire sent to 355 people who had at least once purchased or used services offered online from any site associated with aviation. The questionnaire was analyzed using structural equation modeling. Utilizing Amos V.22, the study hypotheses were assessed. The empirical results show that social media engagement, brand experience, brand preference and conversion rate optimization were all impacted by AI. Conversion rate optimization and social media interaction also have an impact on brand preference and experience. Re-purchase intention is influenced by brand preference and brand experience. Additionally, the association between AI and re-purchase intention was mediated by social media engagement, brand experience, conversion rate optimization and brand preference. The study will support airline companies in developing AI and creating more effective branding and marketing campaigns to increase customer intention to re-purchase. This study discovered that the use of AI in marketing significantly improved brand preference, which subsequently affected consumers’ desire to make additional purchases. Furthermore, to improve long-term commercial performance and brand attractiveness, the airline should focus brand-building efforts on AI. Thus, the airline ought to make greater investments in AI and booking service technology, both to draw in new business and to strengthen existing ones.

Raed Alkaied

Shadi Khattab

Ishaq Al Shaar

Mohammed Abu Zaid

Sakher Al-Bazaiah

The impact of artificial intelligence on re-purchase intentions, the mediation approach

人工智能对再购买意向的影响：中介方法

More and more companies are influenced by the rapid development of technology (Industry 4.0/5.0 concept), are embracing digital transformation processes. The introduction of information systems makes it possible to accumulate a large amount of data about the company’s activities. Study of such information expands the opportunities for applying a data-driven approach to business process management (BPM). Processing and studying data from event logs using process mining methods make it possible to build digital models of business processes which turn out to be a useful source of information when carrying out analysis, modeling and reengineering within the framework of the process approach. In this paper, we develop a method for building a business process model based on a hidden Markov model, taking into account the restrictions imposed by the subject area. The use of a hidden Markov model allows us to use the apparatus of probability theory and mathematical statistics to analyze business processes, as well as to solve classification and clustering problems. This article describes the capabilities of a data-driven approach to business process management and demonstrates examples of the practical application of the method to solve business challenges: drawing a dependency graph that can be used to identify discrepancies between actual and expected execution, as well as a method for predicting the outcome of a business process based on the sequence of observed events.

Artem Varnukhov

Hidden Markov model: Method for building a business process model

隐马尔可夫模型：构建业务流程模型的方法

In the process of import substitution, higher educational institutions face several challenges in transitioning from the predominant use of foreign software to domestic alternatives. These challenges include a lack of user experience with domestic digital solutions, difficulty in transferring data between systems and other issues. The difficulties associated with the transition period create resistance to the digital transformation process. Research on import substitution in universities has identified three main themes: the challenges and risks associated with switching to domestic software, exploring the feasibility of a complete transition to Russian software and providing recommendations for selecting Russian solutions. This study aims to identify the factors that influence the adoption of import substitution software products in higher education. The article proposes a structural model to identify the factors that contribute to successful software import substitution. The model is based on the theories of innovation diffusion and technology adoption, and it was developed using SmartPLS software. The model is based on data collected from a survey of professors and staff at the Ural State University of Economics. The results of the study indicate that the attitude towards adopting import substitution software depends on several factors, including the personal characteristics and innovative features of the software. The most significant determinants of a positive attitude towards transitioning to domestic software include user involvement and self-efficacy. In addition, a positive perception of the need for import substitution can influence individual acceptance of transitioning to Russian software and recognizing import substitution as an economic policy of the country. The theoretical significance of the study lies in its proposal of an original model for identifying the determinants of successful software import substitution that differentiates between individual acceptance and public recognition of software import substitution. The findings of the study could be useful to university management in planning and implementing measures for an import substitution strategy.

Svetlana Begicheva

Antonina Begicheva

Dmitry Nazarov

Constructing a model to identify the determinants of successful software import substitution

构建模型以识别成功软件进口替代的决定因素

The article introduces a long-term investment algorithm that identifies optimal solutions in lower dimensional spaces constructed through principal component analysis or kernel principal component analysis. Portfolio weights optimization is carried out using the Markowitz method. Hyperparameters of the model include window size, smoothing parameter, rebalancing period and the fraction of explained variance in dimensionality reduction methods. The algorithm presented incorporates weights regularization taking into account portfolio rebalancing transaction costs. Hyperparameters’ selection is based on the Martin coefficient, which allows us to consider the maximum drawdown for the suggested algorithms. The results demonstrate that the proposed algorithm, trained from 1990 to 2016, shows higher returns and Sharpe ratios compared to the S&amp;P 500 benchmark from 2017 to 2022. This indicates that weights optimization can improve the algorithm’s performance through rebalancing.

ISSN	2587-814X, 2587-8158
h-index	11

涉及主题	计算机科学经济数学业务工程类政治学法学统计人工智能操作系统生物营销程序设计语言物理机器学习管理哲学量子力学财务
出版信息	出版商: National Research University, Higher School of Econoimics，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2017，原创研究文献占比: 0.00%，自引率:N.A.， Gold OA占比: 0.00%

Business Informatics

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