This article investigates the impacts of the COVID-19 pandemic and their proactive mediation by adaptive operational decisions in different network design structures in anticipation of and during the pandemic. In generalized terms, we contribute to the understanding of the effect of preparedness and recovery decisions in a pandemic setting on supply chain operations and performance. In particular, we examine the impact of inventory pre-positioning in anticipation of a pandemic and the adaptation of production-ordering policy during the pandemic. Our model combines three levels, which is not often seen jointly in operations management literature, i.e., pandemic dynamics, supply chain design, and operational production-inventory control policies. The analysis is performed for both two- and three-stage supply chains and different scenarios for pandemic dynamics (i.e., uncontrolled propagation or controlled dispersal with lockdowns). Our findings suggest that two-stage supply chains exhibit a higher vulnerability in disruption cases. However, they are exposed to a lower system inertia and show positive effects at the recovery stage. Supply chain adaptation ahead of a pandemic is more advantageous than during the pandemic when specific operational recovery policies are deployed. We show that it is instructive to avoid simultaneous changes in structural network design and operational policies since that can destabilize the production-inventory system and result in higher product shortages.
In this paper, we describe the concept and the implementation of an agent-based supply chain simulation model designed as a teaching aid to support introductory classes in supply chain management. The model helps students to understand the key cost and service level trade-offs in inventory policy design and supplier selection. The model is implemented using Anylogistix software.
There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gained momentum in the recent years among potential users. Connected and Autonomous Electric Vehicle (CAEV) technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking. Therefore, Traffic Flow Prediction (TFP) is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning (DL) techniques. In this view, the current research paper presents an artificial intelligence-based parallel autoencoder for TFP, abbreviated as AIPAE-TFP model in CAEV. The presented model involves two major processes namely, feature engineering and TFP. In feature engineering process, there are multiple stages involved such as feature construction, feature selection, and feature extraction. In addition to the above, a Support Vector Data Description (SVDD) model is also used in the filtration of anomaly points and smoothen the raw data. Finally, AIPAE model is applied to determine the predictive values of traffic flow. In order to illustrate the proficiency of the model's predictive outcomes, a set of simulations was performed and the results were investigated under distinct aspects. The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.
This work analyses the firm failure process stages using the Bayesian network as a modelling tool because it allows us to identify causal relationships in the firm profile. We use publicly available data on French, Italian and Russian firms containing five samples corresponding to periods from one to five years before observation. Our results confirm that there is a difference between the stages of the failure process. For firms at the beginning of a lengthy process (3–5 years before observation), cumulative profitability is the key that determines liquidity. Then, as the process develops, leverage comes to the fore in the medium term (1–2 years before observation) for economies with more uncertainty. This factor limits the opportunities for making a profit, leading to further development of the failure. There are also national specifics that are caused, firstly, by the level of economic development and, secondly, economic policy uncertainty.
The paper discusses the issues of industrial clusters analysis. Initially, the authors explore theoretical approaches to understanding clusters phenomenon, their identification and analysis. Looking at industrial clusters as network structures connected by various forms of interaction between members, such as ownership linkages, transactions, the presence of common counterparts, participation in arbitration processes, the authors propose to visualize clusters using social network analysis metrics. This approach helps to address one of the main difficulties when contacting the members of industrial clusters, for a subsequent survey or in-depth interviewing. The analysis concludes with a discussion of the proposed method as a way to identify cluster members and determine the most significant ones that are the primary nodes of the network. These key members usually possess enough relevant information about the structure, coordination mechanisms, general strategy, and cluster management system. Therefore, it is possible to limit the list of interviewed respondents without substantial loss of empirical data quality. A case of the textile industry cluster presented in the paper confirms the applicability of social network analysis to the visualization and description of industrial clusters.
Performance evaluation enables decision makers (DMs) to have a better view about the weaknesses and strengths of leading units to improve efficiencies as a crucial goal. Data envelopment analysis (DEA) is the most popular technique to measure performance efficiency of decision making units (DMUs). However, conventional DEA is unable to consider uncertainty of input and output data in the evaluations. In this study, in order to address uncertainty in data, a robust credibility DEA (RCDEA) model has been introduced. First, a fuzzy credibility approach is used to construct fuzzy data. Then, a robust optimization approach is applied to consider uncertainty in constructing fuzzy sets. Moreover, perturbation level is considered as exact and fuzzy values. To illustrate the capability of the proposed model, 28 hospitals are evaluated in northwestern region of Iran and results are analyzed. According to the results, as perturbation degree increases, DMUs get normalized lower efficiencies and vise-versa.
The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations, particularly security and privacy challenges. This systematic literature review aims to identify similarities, differences, main attacks, and countermeasures in the various paradigms mentioned. The main determining outcome points out the essential security and privacy threats. The presented results also outline important similarities and differences in Cloud, Edge, and Fog computing paradigms. Finally, the work identified that the heterogeneity of such an ecosystem does have issues and poses a great setback in the deployment of security and privacy mechanisms to counter security attacks and privacy leakages. Different deployment techniques were found in the review studies as ways to mitigate and enhance security and privacy shortcomings.
Many aspects of bankruptcy have not yet been thoroughly studied, among such issues are the causes that lead to bankruptcy at various stages of the company’s lifecycle. We hypothesize that the most significant factors in- fluencing the probability of company bankruptcy at a particular stage of its lifecycle are those the effectiveness of which is at the lowest level at this stage. These factors include the external environment, the quality of financial, and corporate governance. The methodology of the research consists of the meth- ods PLS-SEM (to determine the impact of factors on bankruptcy) and DEA (to evaluate the effectiveness of factors usage). The empirical database in- cludes 376 Russian public companies. The simulation results support the hypothesis. We also revealed that the external environment exerts a more powerful effect on the probability of bankruptcy at the stage of growth. The role of financial management increases from the initial stage to the final stage of the life cycle. Corporate governance is less important than the other two factors, but its impact is significant at the stage of growth.
The main objective of this paper is to explore the impact of the COVID19 pandemic on customer loyalty factors in the Russian e-commerce market. The pandemic has dramatically changed consumer behavior in e-commerce. Russia’s e-commerce has grown significantly since 2020 due to the COVID-19 pandemic. The new customers entering the online market and an increase in online shopping frequency due to the quarantine are among the reasons for the growth in the financial value of Russia’s e-commerce. There was a 44% growth of the industry in 2020 compared to 2019. To explore the possible impact of the COVID-19 pandemic on customer loyalty factors, quantitative empirical data was gathered in 2019 and 2020, with 836 and 926 accurate observations respectively. Methods of exploratory factor analysis, confirmatory factor analysis, and the t-test were used to analyze the data along with the validity and reliability indices. After confirming the CFA model, nine constructs affecting consumer loyalty in 2019 and 2020 were examined to investigate possible changes in the mean values of their indicators. The results showed that factors Consumer satisfaction, Ease of making online purchases, e-WOM, and Number of reviews have a statistically significant difference in the mean value of the indicators between Pre- and the COVID-19 era. These findings can help Russian online business managers to adapt to changes in consumer behavior. To enhance e-WOM, having a platform to get customer feedback and understand their perception about the service and product is recommended.
The paper examines Russian SMEs’ practices of responsible behavior directed at environmental problems. The research goal was to observe those practices and detect drivers stimulating environmental responsibility of Russian SMEs. The research method used in the study is an interview. A comfortable sample of 77 SMEs’ representatives was established using the snowball method. The paper used content-analysis for analyzing data from the interviews. A unit of count was a mention of environment-related responsible practices or activities, held by an SME following one of seven directions of social responsibility according to the ISO—26000:2010 “Environmental issues” (ISO 26000:2010 2012). From respondents’ answers social and environmental practices of SMEs were identified. It is argued that environmental practices of Russian SMEs are less numerous and diverse than social practices, and mostly constitute sporadic, one-time actions. An external environment can hardly be considered a driver of responsible behavior in case of Russian SMEs. In industries that presume environmentally harmful operations, a leading role remains with the state authorities that enact environmental protection legislation and industry-specific norms aimed at stimulating responsible behavior and performance. The study revealed cases of SMEs’ where personal choices of business owners and top-managers form a major determining factor of responsible behavior and its priorities. To promote SMEs’ environmental responsibility and projects, authors suggest to develop environmental education and training; to increase awareness of environmental responsibility among the country’s population and entrepreneurs; to adjust tax policies towards SMEs based on their resource consumption and polluting practices etc.
The article deals with the general principles of construction and architecture of modern cognitive centres to support strategic decision-making on socio-economic development and its basic components based on a generalised simulation model of the socio-economic system, complemented by modern knowledge-oriented cognitive technologies, as well as data analysis and scenario analysis techniques. Approaches to the construction of multi-paradigm simulation models based on the principles of a composite combination of system dynamics and agent-based simulation modeling are proposed, which allow describing the processes of development and self-organization in socio-economic systems. The promising directions for the development of systems modeling technology and the construction of multi-model complexes in strategic development projects based on the principles of stratification, ontological modeling, scenario planning are outlined.
Abstract Purpose – This paper outlines the current and future influence of digitalization on corporate parenting styles of multinational corporations. Design/methodology/approach – We employed conceptual modeling in this study. Findings – We identified five types of corporate parenting styles (Hypnos, Cronus, Rhea, Zeus, and Athena). The overall impact of digitalization on corporate parenting styles is related to new, formidable opportunities for decreasing costs and increasing the efficiency of the intra-corporate transfer of knowledge and talent. Furthermore, digitalization leads to greater tightness in subsidiaries’ performance targets, and greater intensity of control over subsidiaries’ activities, to lower degrees of subsidiary autonomy and a lower level of trust between the corporate headquarters (CHQ) and subsidiary managers. These effects endanger the existence of two corporate parenting styles (Hypnos and Athena) and significant changes for other three corporate parenting styles. Practical implications Digitalization may lead to more homogeneous corporations, with the lower variety of corporate parenting styles and the greater centralization of decision-making in corporate and regional headquarters and stronger control on operations and performance of subsidiaries. Increased opportunities of horizontal value transfer (knowledge) within the corporation will present an additional competitive advantage of subsidiaries of multinational corporations. The increased ability and willingness of corporate and regional headquarters of value appropriation from subsidiaries in different forms (profit, revenues, knowledge, and talent) will force subsidiaries to use that additional competitive advantage to become more aggressive competitors in local and global markets.
Provides a multidisciplinary perspective on the role of digitalization for society, economics, management, and education
Presents current research methods for digital technologies in management and organization
Highlights the most relevant topics in the field of digital technologies
This book gathers the papers on digitalization of society, economics and management in post-pandemic period. It shares the latest insights into various aspects of the digitalization of the economy and the consequences of transformation in public administration, business and public life. Integrating a broad range of analytical perspectives, including economic, social and, technological, this interdisciplinary book is particularly relevant for scientists, digital technology users, companies and public institutions.
Distributed Ledger Technology (DLT) is making the first steps toward becoming a solution for the growing number of various decentralized systems world-wide. Unlike pure Blockchain, DLT finds many uses across different industries, including eHealth, finance, supply chain monitoring, and the Internet of Things (IoT). One of the vital DLT features is the ability to provide an immutable and commonly verifiable ledger for larger-scale and highly complex systems. Today’s centralized systems can no longer guarantee the required level of availability and reliability due to the growing number of the involved nodes, complicated heterogeneous architectures, and task load, while the publicly available distributed systems are still in their infancy. This paper aims to provide an exhaustive topical review of the state-of-the-art of Distributed Ledger Technology applicability in various sectors. It outlines the importance of the practical integration of technology-related challenges, as well as potential solutions.
We investigate the impact of geopolitical risk (GPR) generated by the Russian-Ukrainian conflict on European and Russian bonds, equity, and global commodity markets. We employ the GPR index and apply the quantile-on-quantile regression approach to the GRP index and financial asset returns. Our findings indicate that (i) most assets are in a mix of negative and positive relationship with GPR; (ii) GPR leads to changes in asset returns during normal market conditions; and (iii) the magnitude and direction of GPR's effect on asset returns depend on the type of market and market conditions.
The sandpile cellular automata, despite the simplicity of their basic rules, are adequate mathematical models of real-world systems, primarily open nonlinear systems capable to self-organize into the critical state. Such systems surround us everywhere. Starting from processes at microscopic distances in the human brain and ending with large-scale water flows in the oceans. The detection of critical transitions precursors in sandpile cellular automata will allow progress significantly in the search for effective early warning signals for critical transitions in complex real systems. The presented paper is devoted to the detection and investigation of such signals based on multifractal analysis of the time series of falls of the cellular automaton cells. We examined cellular automata in square lattice and random graphs using standard and facilitated rules. It has been established that log wavelet leaders cumulant are effective early warning measures of the critical transitions. Common features and differences in the behavior of the log cumulants when cellular automata transit into the self-organized critical state and the self-organized bistability state are also established.
The five articles included in this collection provide novel insights from four different countries (South Africa, Nigeria, South Korea, and the US) on issues related to the nature, scope, and consequences of law and/or ethics violation in healthcare.