Hasil untuk "Real estate business"

Changhao Li, Junwei Yin, Zhilin Zeng et al.

Generative recommendation (GR) has recently emerged as a promising paradigm for industrial recommendations. GR leverages Semantic IDs (SIDs) to reduce the encoding-decoding space and employs the Next Token Prediction (NTP) framework to explore scaling laws. However, existing GR methods suffer from two critical issues: (1) a \textbf{seesaw phenomenon} in multi-business scenarios arises due to NTP's inability to capture complex cross-business behavioral patterns; and (2) a unified SID space causes \textbf{representation confusion} by failing to distinguish distinct semantic information across businesses. To address these issues, we propose Multi-Business Generative Recommendation (MBGR), the first GR framework tailored for multi-business scenarios. Our framework comprises three key components. First, we design a Business-aware semantic ID (BID) module that preserves semantic integrity via domain-aware tokenization. Then, we introduce a Multi-Business Prediction (MBP) structure to provide business-specific prediction capabilities. Furthermore, we develop a Label Dynamic Routing (LDR) module that transforms sparse multi-business labels into dense labels to further enhance the multi-business generation capability. Extensive offline and online experiments on Meituan's food delivery platform validate MBGR's effectiveness, and we have successfully deployed it in production.

Changshuai Wei, Phuc Nguyen, Benjamin Zelditch et al.

The standard A/B testing approaches are mostly based on t-test in large scale industry applications. These standard approaches however suffers from low statistical power in business settings, due to nature of small sample-size or non-Gaussian distribution or return-on-investment (ROI) consideration. In this paper, we (i) show the statistical efficiency of using estimating equation and U statistics, which can address these issues separately; and (ii) propose a novel doubly robust generalized U that allows flexible definition of treatment effect, and can handles small samples, distribution robustness, ROI and confounding consideration in one framework. We provide theoretical results on asymptotics and efficiency bounds, together with insights on the efficiency gain from theoretical analysis. We further conduct comprehensive simulation studies, apply the methods to multiple real A/B tests at LinkedIn, and share results and learnings that are broadly useful.

Marina Soares Marinho, Daniela Vianna, Livy Real et al.

This study presents the Jusbrasil Study on the Use of General-Purpose AIs in Law, proposing an experimental evaluation protocol combining legal theory, such as material correctness, systematic coherence, and argumentative integrity, with empirical assessment by 48 legal professionals. Four systems (JusIA, ChatGPT Free, ChatGPT Pro, and Gemini) were tested in tasks simulating lawyers' daily work. JusIA, a domain-specialized model, consistently outperformed the general-purpose systems, showing that both domain specialization and a theoretically grounded evaluation are essential for reliable legal AI outputs.

Tim J. Boonen, Engel John C. Dela Vega, Bin Zou

This paper considers an insurer with two collaborating business lines, and the risk exposure of each line follows a diffusion risk model. The manager of the insurer makes three decisions for each line: (i) dividend payout, (ii) (proportional) reinsurance coverage, and (iii) capital injection (from one line into the other). The manager seeks an optimal dividend, reinsurance, and capital injection strategy to maximize the expected weighted sum of the total dividend payments until the first ruin. We completely solve this problem and obtain the value function and optimal strategies in closed form. We show that the optimal dividend strategy is a threshold strategy, and the more important line always has a lower threshold to pay dividends. The optimal proportion of risk ceded to the reinsurer is decreasing with respect to the aggregate reserve level for each line, and capital injection is only used to prevent the ruin of a business line. Finally, numerical examples are presented to illustrate the impact of model parameters on the optimal strategies.

Mohammad Derakhshan, Paolo Ceravolo, Fatemeh Mohammadi

This paper investigates the effectiveness of GPT-4o-2024-08-06, one of the Large Language Models (LLM) from OpenAI, in detecting business process anomalies, with a focus on rework anomalies. In our study, we developed a GPT-4o-based tool capable of transforming event logs into a structured format and identifying reworked activities within business event logs. The analysis was performed on a synthetic dataset designed to contain rework anomalies but free of loops. To evaluate the anomaly detection capabilities of GPT 4o-2024-08-06, we used three prompting techniques: zero-shot, one-shot, and few-shot. These techniques were tested on different anomaly distributions, namely normal, uniform, and exponential, to identify the most effective approach for each case. The results demonstrate the strong performance of GPT-4o-2024-08-06. On our dataset, the model achieved 96.14% accuracy with one-shot prompting for the normal distribution, 97.94% accuracy with few-shot prompting for the uniform distribution, and 74.21% accuracy with few-shot prompting for the exponential distribution. These results highlight the model's potential as a reliable tool for detecting rework anomalies in event logs and how anomaly distribution and prompting strategy influence the model's performance.

Keke Vivian Onyinye, Okafor Johnbosco Ikenna, Isaac Promise Chibuzor

The concept of smart housing in the design of residential homes is one that has developed extensively in terms of modern-day city planning, even when it comes to developing cities like Awka. Smart homes are built on advanced technological development, sustainable design principles, and user-centric facilities aimed at creating more efficient, comfortable, and eco-friendly living spaces. The aim of this study is to assess constraining factors to affordable smart housing development in Awka, Anambra State, Nigeria. This study employs a mixed-methods research design, combining both quantitative and qualitative approaches to investigate the constraining factors to affordable smart housing development in Awka. This study employed a quantitative research design. The population for the research comprises construction professionals and other relevant stakeholders in Awka, Anambra State. Specifically, the study population includes 97 construction professionals, 135 estate surveyors and valuers, and 400 Awka residents. The data were presented using tables and examined through mean score calculations and percentage analyses. The research revealed that the existing smart housing projects were perceived as unaffordable. The study identified key factors hindering the development of affordable smart housing in Awka. Lack of infrastructure emerged as a major constraint. The high cost of smart technologies was also identified as a crucial obstacle. The study recommends that government should prioritize the development of basic infrastructure to support smart housing technologies. This includes improving power supply, internet connectivity, and other necessary utilities. A comprehensive infrastructure development plan should be created and implemented in phases, focusing first on areas designated for smart housing projects.

Seyed Reza Seyed Nezhad Fahim

The purpose of this study is to investigate the relationship between financialization and financing maturity mismatch concerning the debt issues faced by Iranian firms, aiming to provide insights for managers and policymakers. The statistical sample consists of 143 active firms listed on the Tehran Stock Exchange over a ten-year period, from 2013 to 2022. The regression method was employed to estimate the models and test the research hypotheses. The findings indicate no significant relationship between financialization and the long-term use of short-term debt. However, distinct results emerged when firms were categorized into large and small entities. In large firms, financialization appears to positively affect the long-term use of short-term debt, in contrast to smaller firms. Additionally, financial constraints do not significantly influence the propensity of firms to utilize short-term debt and do not incentivize increased willingness among these firms to rely on short-term financing in the long term. Furthermore, financialization leads to a decrease in investment in productive assets, with firms that are more inclined toward financialization utilizing less internal financing. While previous studies on maturity mismatch have highlighted factors such as external regulations, the macroeconomic environment, and internal corporate governance, this study addresses a gap in the research concerning the discrepancy between corporate debt and investment horizons. It provides empirical evidence at the firm level and investigates the underlying mechanisms through which financialization influences the long-term use of short-term debt.Keywords: Capital Structure, Debt Maturity Structure, Financialization, Tangible Long-Term InvestmentsJEL Classification: G11، G31، P45  IntroductionAccording to the principle of matching investment and financing maturities, the maturity of a firm's debt should correspond to the maturity of its assets (Chen et al., 2023). The long-term use of short-term debt (LUSD) is a critical aspect of debt maturity mismatch, wherein short-term debt is allocated to support long-term investments. Analyzing the factors influencing LUSD is essential for gaining a comprehensive understanding of corporate financing decisions. The phenomenon of corporate financialization is prevalent globally and is closely linked to corporate investment and financing practices (Cao et al., 2022). Financialization may seem to reduce investment in fixed assets, potentially leading to diminished firm performance and economic recession (Tori & Onaran, 2018). Consequently, firms may encounter financing constraints, making it more challenging to secure long-term loans and resulting in an increased reliance on LUSD. Conversely, financialization may also be driven by hedging strategies and the pursuit of higher returns, which can alleviate financing constraints and improve firm performance, thereby reducing reliance on LUSD to some extent (Gong et al., 2023). Thus, the relationship between corporate financialization and LUSD remains ambiguous and warrants further investigation. This study aims to explore the relationship between financialization and financing maturity mismatch concerning the prevailing debt issues among Iranian firms, with the intent of providing insights for managers and policymakers. The findings are expected to offer valuable guidance for aligning fiscal policies, financing strategies, and investment initiatives. Materials & MethodsThe dependent variable of this study is the long-term use of short-term debt (LUSD), measured as the difference between the ratio of short-term liabilities to total liabilities and the ratio of short-term assets to total assets. Financialization serves as the independent variable, defined as the ratio of total financial assets to total assets. Financial assets encompass short-term and long-term investments, non-trade receivables, prepayments, and investments in real estate. Additionally, financial constraints are incorporated as an interactive variable, represented by a dummy variable. Firms with a financial cost-to-total debt ratio exceeding the median are classified as facing resource acquisition restrictions and assigned a value of one; otherwise, they are assigned a value of zero. The sample includes active firms listed on the Tehran Stock Exchange (TSE). The sample includes 143 firms over a ten-year period from 2013 to 2022. To explore the relationships among the variables, multiple linear regression analysis was conducted. The data were collected from firms' financial reports, the Codal system, and other reliable financial sources. Findings The findings indicate that, contrary to existing literature, there is no significant relationship between financialization and the long-term use of short-term debt (LUSD). However, when firms are categorized into large and small entities, distinct results emerge. In large firms, financialization has a positive effect on LUSD, whereas in small firms, financialization exerts a negative impact on LUSD. Additionally, the results suggest that financial constraints do not significantly influence the use of short-term debt and do not serve as an incentive for firms to increase their reliance on short-term financing in the long term. Furthermore, financialization is associated with a reduction in investment in productive assets. The findings also indicate that financialization diminishes firms' willingness to obtain internal financing. This suggests that firms inclined toward financialization are less likely to seek internal funds, thereby increasing their dependence on external borrowing. Discussion & Conclusion Given the high level of financialization in the sample firms, it is suggested that there should be an optimal degree of financialization, with careful consideration of the economic consequences of excessive financialization. In response to the current situation, it is recommended that enterprises focus on their core business, clarify their development priorities, and engage in financialization activities strategically at the micro level. Additionally, participation in corporate governance is essential. At the macro level, while guiding the development of the financial industry, it is also necessary to stimulate innovation in the economic value of enterprises and enhance their overall strength. Given the negative impact of financialization on investment in productive assets, it is crucial to foster a greater willingness and confidence among enterprises to invest in production units and encourage investment in the real economy. Governments and local institutions should effectively support enterprises by implementing robust policies for the real industry and creating a favorable business environment. Considering the positive impact of financialization on LUSD in large firms, managers are advised to rely more on long-term financing to better manage financial risk and working capital, thereby preventing default risk. Furthermore, in light of the negative impact of financialization on domestic financing—which diminishes its benefits—it is essential to streamline both direct and indirect financing channels for companies, improve the capital market environment, and expand the routes for long-term capital supply in the market.

Aleksei Lvovich Semenov, Dmitrii Egorovich Kurbakov, Yuliia Konstantinovna Kosenkova

Установлено, что комплексное развитие территорий является базовым направлением строительной отрасли. Основной целью данной государственной программы является повышение качества жизни граждан, обновление и развитие городской среды на принципах устойчивости, повышение эффективности использования территорий и решение проблем с аварийным жильем. С реализацией инвестиционных проектов связано множество проблем, среди которых социальные, организационно-коммуникационные, финансовые и нормативно-законодательные риски. Несовершенство механизма комплексного развития территорий приводит к множеству конфликтов, требующих классификации, анализа и принятия управленческих мер с целью их решения. Для этого следует разработать систему, позволяющую применять машинные алгоритмы, т.е. автоматизированный классификатор конфликтов. С этой целью на базе иерархического анализа и пространственной кластеризации на основе иерархической плотности с учетом шума разработаны алгоритмы типизации и кластеризации конфликтных ситуаций. Иерархический анализ создает древовидную структуру кластеров, что может помочь при необходимости создать общую структуру информации о проектах и возникших противоречиях. Плотностная кластеризация позволяет определить «конфликты-вылеты» и необходима для анализа внутренней структуры групп типовых конфликтов. Кроме того, с помощью системы весов предложен способ определения интегральной оценки значимости, т.е. степени влияния возникших противоречий на осуществление проекта.

Нели Димитрова

Устойчивото строителство е ключов фактор в прехода към екологична икономика, като съчетава иновации, ефективно управление на ресурсите и използването на екологични материали. Настоящото изследване разглежда опита на Белгия в областта на устойчивото строителство, като акцентира върху регулациите, базирани на кръговата икономика и енергийната ефективност. Анализирани са механизми като използването на EPC сертификати, които оценяват и класифицират сградите според енергийните им характеристики и представянето им в отделни категории показатели. Методологията включва сравнителен анализ на нормативната рамка във Фландрия, Валония и Брюксел, както и преглед на конкретни мерки в провинция Лимбург, където до 2040 г. всички обществени сгради трябва да постигнат енергиен етикет C. Допълнително са разгледани задължителните EPC сертификати за административни сгради, които влизат в сила поетапно от 2025 г. Резултатите показват, че прилагането на подобни мерки в България би могло да ускори развитието на устойчивото строителство, да подобри енергийната ефективност, да намали въглеродния отпечатък и да допринесе за повишаване на конкурентоспособността на строителния сектор. Опитът на Белгия може да бъде използван като модел за разработване на ефективни регулации и стимули за инвестиции в устойчива инфраструктура.

Francesca Abastante, Margherita Penza

The idea of sustainable development is not new; however, the scientific literature has revived and renewed interest in this topic, emphasizing the need for a holistic approach that considers the complex interaction between buildings, their surroundings and the urban ecosystem. In this context, various methods and tools have been developed to assess and improve urban sustainability. This paper focuses on Neighbourhood Sustainability Assessment Tools (NSATs) as comprehensive tools that embrace both qualitative and quantitative dimensions, in line with the current sustainability paradigm. Starting from the existing NSATs, the present research proposes an implemented tool able to guide the design of urban projects considering a broad sustainability perspective. In order to do that, this paper applies a multi-methodological framework involving the analysis of the most relevant European NSATs, the indepth study of academic literature, the study of specific Italian tools and the inclusion of questionnaires to propose an innovative assessment model. The paper concludes with an analysis of the strengths, limitations and future prospects of the proposed model.

Nguyen Tran Tuan

In the context of increasing deforestation and climate change, this study aims to assess the impact of changes in forest land use on carbon stocks in the mountainous region of Northern Vietnam, one of the key ecological regions but under great pressure from economic development and deforestation. Using remote sensing data, geographic information systems (GIS), and methods to calculate wood volume, the study looked at changes in forest size, carbon storage, and CO2 absorption in 9 provinces from 2000 to 2020. The results show that the total forest area in the region has decreased by more than 4500 km2 in two decades, leading to significant declines in biomass volume, carbon stocks, and CO2 absorption capacity. Some provinces, such as Tuyen Quang, Son La, and Lao Cai, recorded the largest forest degradation, while Lang Son and Lai Chau showed positive recovery trends. Regional carbon emissions exceeded 1.3 million tons during the study period, concentrated mainly in the 2000–2010 period. These changes reflect the simultaneous impacts of land conversion, infrastructure development, resource exploitation, and livelihood pressures. The study provides scientific evidence on the link between deforestation and the carbon balance and offers policy implications for sustainable forest resource management and greenhouse gas emission mitigation in Vietnam.

Robert A. Simons, Spenser J. Robinson, Daniel J. Simons

In February 2023, a Norfolk Southern (NS) train derailed in East Palestine Ohio. Several rail cars crashed off the tracks, releasing hazardous chemicals into two local creeks. Two days later, after calling for an evacuation of homes within one mile, the railroad performed a controlled burn-off of the chemicals, releasing a toxic plume spanning miles in multiple directions. We evaluate the effect of these events on house prices. For this case we conducted both a hedonic regression analysis and contingent valuation (CV) analysis. The regression analysis included over 3,000 house sales from 2018 to 2024, of which 98 sales took place in the Value Assurance Program (VAP) area soon after the chemical burn-off. Our CV looked at five different fact patterns with over 1,000 online survey responses. Overall, we found that residential sales prices in the VAP were down approximately 14% after the release event, compared with control properties. Additionally, results showed that impacted properties remained approximately 25% longer on the market than the control homes; this paper is among the first to show that days on market are impacted by a contamination event.

Sven Weinzierl, Sandra Zilker, Sebastian Dunzer et al.

Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are used to analyse and improve processes efficiently. Three frequent examples of using ML are providing decision support through predictions, discovering accurate process models, and improving resource allocation. This paper organises the body of knowledge on ML in BPM. We extract BPM tasks from different literature streams, summarise them under the phases of a process`s lifecycle, explain how ML helps perform these tasks and identify technical commonalities in ML implementations across tasks. This study is the first exhaustive review of how ML has been used in BPM. We hope that it can open the door for a new era of cumulative research by helping researchers to identify relevant preliminary work and then combine and further develop existing approaches in a focused fashion. Our paper helps managers and consultants to find ML applications that are relevant in the current project phase of a BPM initiative, like redesigning a business process. We also offer - as a synthesis of our review - a research agenda that spreads ten avenues for future research, including applying novel ML concepts like federated learning, addressing less regarded BPM lifecycle phases like process identification, and delivering ML applications with a focus on end-users.

Julian Neuberger, Han van der Aa, Lars Ackermann et al.

Machine-learning based generation of process models from natural language text process descriptions provides a solution for the time-intensive and expensive process discovery phase. Many organizations have to carry out this phase, before they can utilize business process management and its benefits. Yet, research towards this is severely restrained by an apparent lack of large and high-quality datasets. This lack of data can be attributed to, among other things, an absence of proper tool assistance for dataset creation, resulting in high workloads and inferior data quality. We explore two assistance features to support dataset creation, a recommendation system for identifying process information in the text and visualization of the current state of already identified process information as a graphical business process model. A controlled user study with 31 participants shows that assisting dataset creators with recommendations lowers all aspects of workload, up to $-51.0\%$, and significantly improves annotation quality, up to $+38.9\%$. We make all data and code available to encourage further research on additional novel assistance strategies.

Ian Brooks, Laura Harrison, Mark Reeves et al.

This research explores the use of History to enhance education in the field of ICT For Sustainability ICT4S in response to a challenge from the ICT4S 2023 conference. No previous studies were found in ICT4S but the literature on History and Education for Sustainable Development is reviewed. An ICT4S lecturer collaborated with History lecturers to add an historic parallel to each weeks teaching on a Sustainable Business and Computing unit for final year undergraduate BSc Business Computing students. A list of the topics and rationale is provided. Student perceptions were surveyed before and after the teaching and semi-structured interviews carried out. A majority of students saw relevance to their degree and career. There was an increase in the proportion of students with interest in History. The paper explores the lessons learned from the interdisciplinary collaboration, including topic choice, format and perceived value. The project has enhanced the way we approach our subjects as computing and history educators. We believe this is the first empirical, survey-based study of the use of history to enhance ICT4S education. The team will extend the research to a larger unit covering a wider range of computing degrees.

Nafiseh Soveizi, Fatih Turkmen, Dimka Karastoyanova

Today, the number of data-intensive and compute-intensive applications like business and scientific workflows has dramatically increased, which made cloud computing more popular in the matter of delivering a large amount of computing resources on demand. On the other hand, security is a critical issue affecting the wide adoption of cloud technologies, especially for workflows that are mostly dealing with sensitive data and tasks. In this paper, we carry out a review of the state-of-the-art on how security and privacy concerns in scientific and business workflows in cloud environments are being addressed and identify the limitations and gaps in the current body of knowledge in this area. In this extensive literature review, we first present a classification of the state-of-the-art security solutions organized according to the phases of the workflow life cycle they target. Based on our findings, we provide a detailed review and classification of the most relevant available literature focusing on the execution, monitoring, and adaptation phases of workflows. Finally, we present a list of open research issues related to the security of cloud-based workflows and discuss them.

Félix Lirio-Loli, William Dextre-Martínez

Introduction: This study analyzes the scientific production in business administration in scientific articles based on modeling partial least squares structural equations (Partial Least Squares Structural Equation Modeling PLS-SEM) in the 2011-2020 period. Methodology: The study is exploratory - descriptive and has three phases: a) Selection of keywords and search criteria; (b) Search and refinement of information; c) information analysis. A method of bibliometric review of the specific literature has been used based on the analysis of predefined indicators and completed with a qualitative content synthesis. Results: A total of 167 publications were analyzed, making correlations from the year, search criteria, authors, impact factor by quartile, and by citation variables. More outstanding scientific production comes from Scopus under the search criteria ((pls AND sem) OR "partial least squares") AND (business OR management), being the figure of 4,870 scientific articles, while Web of Science accumulates 3,946 articles Conclusion: There has been a progressive growth in scientific articles with the PLS-SEM technique from 2011 to 2020. Scopus, compared to WoS, presents a more significant number of scientific productions with this statistical approach. The authors who register scientific articles demonstrate a high H index; in addition, there is an important number of scientific articles with a PLS-SEM approach in universities in Malaysia that could be related to the expansion of higher education in that country, as well as in Singapore, Taiwan, and Indonesia. Finally, business administration, accounting, and economics are outstanding scientific production.

Doszyń Mariusz

The purpose of this paper is to estimate econometric models with sample and prior information. Prices of land property for residential development in Szczecin are modeled (the price level was determined for 2018). Modeling property prices only based on sample data generates numerous problems. Transaction databases from local real estate markets often contain a small number of observations. Properties are frequently similar, which results in low variability of property characteristics, and thus – low efficiency of parameter estimators. In such a situation, the impact of some features cannot be estimated from the sample data. As a solution to this problem, the paper proposes econometric models that consider prior information. This information can be, for example, in the form of property feature weights proposed by experts. The prior information will be expressed in the form of stochastic restrictions imposed on the model parameters. In the simulation experiment, the predictive power of mixed estimation models is compared with two kind of models: OLS models and model with only prior information. It turned out that mixed estimation results are superior with regard to formal criteria and predictive abilities.

Thomas Ashaolu, Mustapha Bello

Concerns over valuation accuracy and variance cannot be over-flogged, given the somewhat fluid nature of these concepts. It is however, more apt to dig into their fundamental causative factors. This paper realizes that specialist valuer or appraiser has a chain of sequential tasks anchored on his distinguishing competencies. At the heart of this is sufficient knowledge of the attributes of his subject of valuation. Twenty-two (22) Nigerian valuers based within Lagos Metropolis were made to carry out valuation assessment of selected landed and non-landed property assets and were also examined on their perception of the adequacy of their acquired Body of Knowledge (BOK) relevant to each asset category. The variation/dispersion in their valuations is revealed by the Standard Deviation of the distribution, for Landed Property, being 7.77 while that of Non-Landed Property is 32.24; By employing the 10% maximum variation rule of Glover (1985), 9% of the valuers fall outside the limit in respect of Landed Property whereas, the figure rose to 64% for Non-Landed Property assets. This is indicative of remarkably higher internal inconsistencies among respondent valuers on Non-Landed Property Assets. Multiple regression analysis of the results indicated that all the adaptive knowledge variables exert positive influence on valuer’s competence in valuation of both Landed Property and Non-Landed Property Assets. In view of these findings, there is urgent need to review and expand underlying curriculum for training prospective valuers towards aligning theory with practice and enhance their competence across property types.

Wasilij Rudnicki, Iryna Vagner, Iryna Demko et al.

The article analyzes the works of economists who deal with the impact of COVID-19 on the economic development of European countries. The COVID-19 pandemic has already inflicted severe damage on the Ukrainian economy despite relatively mild public health implications so far. The authors analyzed the impact of COVID-19 on the activities of certain sectors of the Ukrainian economy, namely: catering, real estate, the judiciary and more. The direct impact of the pandemic on the economy has been channelled through stopped domestic economic activity in sectors affected by the shutdown, as well as lower demand for Ukrainian exports and lower remittances from abroad. Travel restrictions almost completely stopped local and international tourism. Second round effects stem from reduced household income, redirection of government spending and disruption of investment plans of companies, resulting in lower demand for a wide range of goods and service. For example, reduced electricity demand caused disruptions in energy system balance and lower demand for coal. “Forecast for 2020–2021” has been proposed, developed by experts on the basis of generalized consensus assumptions obtained from the results of the survey. The position of the Union of Ukrainian Entrepreneurs on reducing the tax burden on Ukrainian enterprises during the COVID-19 pandemic and the need to implement radical measures to support business has been revealed. The author has identified short- and medium-term measures that can improve the financial situation of the business after their release from quarantine. Several scenarios for overcoming the post-crisis period for the Ukrainian economy have been proposed.