http://bijournal.hse.ru en-us Copyright 2024 Wed, 25 Sep 2024 10:06:43 +0300 https://bijournal.hse.ru/en/2024--3 Vol 18/965585095.html       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. https://bijournal.hse.ru/en/2024--3 Vol 18/965601920.html       A counterfactual explanation is the generation for a particular sample of a set of instances that belong to the opposite class but are as close as possible in the feature space to the factual being explained. Existing algorithms that solve this problem are usually based on complicated models that require a large amount of training data and significant computational cost. We suggest here a method that involves two stages. First, a synthetic set of potential counterfactuals is generated based on simple statistical models (Gaussian copula, sequential model based on conditional distributions, Bayesian network, etc.), and second, instances satisfying constraints on probability, proximity, diversity, etc. are selected. Such an approach enables us to make the process transparent, manageable and to reuse the generative models. Experiments on three public datasets have demonstrated that the proposed method provides results at least comparable to known algorithms of counterfactual explanations, and superior to them in some cases, especially on low-sized datasets. The most effective generation model is a Bayesian network in this case. https://bijournal.hse.ru/en/2024--3 Vol 18/965613414.html       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. https://bijournal.hse.ru/en/2024--3 Vol 18/966030942.html       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&P 500 benchmark from 2017 to 2022. This indicates that weights optimization can improve the algorithm’s performance through rebalancing. https://bijournal.hse.ru/en/2024--3 Vol 18/966031972.html       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. https://bijournal.hse.ru/en/2024--3 Vol 18/966054131.html 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.