This concise book provides a survival toolkit for efficient, large-scale software development. Discussing a multi-contextual research framework that aims to harness human-related factors in order to improve flexibility, it includes a carefully selected blend of models, methods, practices, and case studies. To investigate mission-critical communication aspects in system engineering, it also examines diverse, i.e. cross-cultural and multinational, environments.
This book helps students better organize their knowledge bases, and presents conceptual frameworks, handy practices and case-based examples of agile development in diverse environments. Together with the authors’ previous books, "Crisis Management for Software Development and Knowledge Transfer" (2016) and "Managing Software Crisis: A Smart Way to Enterprise Agility" (2018), it constitutes a comprehensive reference resource that adds value to this book.
Currently, there is an extensive set of bankruptcy prediction models, but almost all of them are classification based, i.e., they allow to estimate the posterior probability that a particular firm will fail, given its financial characteristics. The expected time to failure is not considered explicitly. On the other hand, there is a survival analysis that deals with the time of the occurrence of the event of interest (while this event may not occur during observation). However, despite its popularity in the medical and technical sciences, survival analysis is relatively rarely used in predicting financial failure. Even when it is applied, most authors use the simplest form of a model. The goal of our work is to evaluate the applicability of survival analysis to bankruptcy prediction. We compare a few state-of-art statistical and machine learning models using a real dataset. Our findings confirm that survival analysis allows (1) to extract from given data valuable information regarding the dynamics of risks and (2) to estimate the impact of features.
Auditors use behavioral red flags (BRFs) to examine which individuals are more prone to unwarranted behavior like corruption and asset misappropriation. Using a rich data set from the Association of Certified Fraud Examiners (ACFE), we analyze the impact of BRFs on loss sizes from asset misappropriation. We control for anti-fraud mechanisms established at the company level and other factors both at the individual and the firm level. Performing an exploratory factor analysis yields six factors for BRFs which capture the principal perpetrator’s situation at the private level and the workplace. A general wheeler-dealer attitude and financial distress significantly increase loss sizes. By contrast, we find no evidence that non-monetary private problems lead to higher losses.
The growing interest and expectations from the blockchain applica-tions attract many analysts to this issue. In what spheres of logistics and supply chain management blockchain is appropriate? What blockchain software solutions are available to companies now? This paper investigates the basic function-ality of the existing software solutions on the market, the comparative analysis of blockchain platforms used for developing the solutions for logistics is also carried out. The main trends of blockchain applications are identified, based on the analysis of the project experience on the use of blockchain, in logistics and supply chain management, in different countries. The problems, limitations and conditions of blockchain implementation are also determined.
The present increase of attention toward blockchain-based systems is currently reaching a tipping point with the corporate focus shifting from exploring the technology potential to creating Distributed Ledger Technology (DLT)-based systems. In light of a significant number of already existing blockchain applications driven by the Internet of Things (IoT) evolution, the developers are still facing a lack of tools and instruments for appropriate and efficient performance evaluation and behavior observation of different blockchain architectures. This paper aims at providing a systematic review of current blockchain evaluation approaches and at identifying the corresponding utilization challenges and limitations. First, we outline the main metrics related to the blockchain evaluation. Second, we propose the blockchain modeling and analysis classification based on the critical literature review. Third, we extend the review with publicly accessible industrial tools. Next, we analyze the selected results for each of the proposed classes and outline the corresponding limitations. Finally, we identify current challenges of the blockchain analysis from the system evaluation perspective, as well as provide future perspectives.
This paper summarizes practices of customer- driven services applied in the leading Russian bank to avoid the impact of financial sanctions (2014–2019). We show how economic sanctions and strict national policies triggered this bank to increase flexibility in customer care to attract more capital from their existing clients. The project comprised three stages: (1) to analyse requirements and to develop ‘‘as-is’’ state of processes; (2) to analyse best practices and to improve processes under the scope of flexibility and customer orientation; (3) to implement the new vision in ‘‘to-be’’ state and final verification. At the third research stage to assess the results of processes improvement in the bank within a year we have applied a set of methods based on data envelopment analysis which provides a multidimensional understanding of processes and new scopes of customer’s value profiles. We have found that process reengineering result could give the contribution already at the first month of implementation and argue the findings could be used to introduce flexible data-driven customer care and improve customer-related processes in organisations worldwide.
We propose an efficient algorithm based on boundary operator equations for the numerical simulation of time-dependent waves in 3D. The algorithm employs the method of difference potentials combined with the (strong) Huygens’ principle (lacunae of the solution). It can handle nonconforming boundaries on regular structured grids with no loss of accuracy and offers sublinear computational complexity.
Performance impacts of ordering and production control policies in the presence of capacity disruptions are studied on the real-life example of a retail supply chain with product perishability considerations. Constraints on product perishability typically result in reductions in safety stock and increases in transportation frequency. Consideration of the production capacity disruption risks may lead to safety stock increases. This trade-off is approached with the help of a simulation model that is used to compare supply chain performance impacts with regard to coordinated and non-coordinated ordering and production control policies. Real data of a fast moving consumer goods company is used to perform simulations and to derive novel managerial insights and practical recommendations on inventory, on-time delivery and service level control. In particular, for the first time, the effect of ‘postponed redundancy’ has been observed. Moreover, a coordinated production–ordering contingency policy in the supply chain within and after the disruption period has been developed and tested to reduce the negative impacts of the ‘postponed redundancy’. The lessons learned from experiments provide evidence that a coordinated policy is advantageous for inventory dynamics stabilization, improvement in on-time delivery, and variation reduction in customer service level.
In this paper we propose an approach for compact storage of big graphs. We propose the preprocessing algorithms of a certain type of graphs which can signi cantly increase the data density on the disc and increase performance of fundamental operations with graphs.
The rapid development of information technologies and their widespread use in various sectors of the economy, including transportation, have led to a massive growth in data flows. However, the paradox of our time is that this data is not used to its full potential to provide companies with powerful impetus for their development. This is especially true at the strategic level, where company executives still mostly make decisions without relying on the recommendations of business intelligence systems. In the management of road transport, this phenomenon can be attributed to the fragmentation of data sources and the vague understanding of the relationships between them. This is also partially due to the diversity in opinions in the expert community on the efficiency of the transport processes. Thus, it is obvious that, without deciding on how to measure efficiency, it is hard to say where the data for calculations should be acquired from and what should be the architecture of a decision support system. Note that the diversity of data sources in the digital age creates prerequisites for various metrics that highlight different aspects of the transportation process.
This book gathers the best papers presented at the first conference held by the Russian chapter of the Association for Information Systems (AIS). 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.
The ripple effect refers to structural dynamics and describes a downstream propagation of the downscaling in demand fulfilment in the supply chain (SC) as a result of a severe disruption. The bullwhip effect refers to operational dynamics and amplifies in the upstream direction as ordering oscillations. Being interested in uncovering if the ripple effect can be a driver of the bullwhip effect, we performed a simulation-based study to investigate the interrelations of the structural and operational dynamics in the SC. The results advance our knowledge about both ripple and bullwhip effects and reveal, for the first time, that the ripple effect can be a bullwhip-effect driver, while the latter can be launched by a severe disruption even in the downstream direction. The findings show that the ripple effect influences the bullwhip effect through backlog accumulation over the disruption time as a consequence of non-coordinated ordering and production planning policies. To cope with this effect, a contingent production-inventory control policy is proposed that provides results in favour of information coordination in SC disruption management to mitigate both ripple and bullwhip effects. The SC managers need to take into account the risk of bullwhip effect during the capacity disruption and recovery periods.
E-commerce market development depends on the configuration of factors which both enable its further development, but might as well hinder its adoption by consumers. In particular, emerging markets provide numerous opportunities for e-commerce; however, they are also associated with specific barriers, limiting the potential for fully exploiting these opportunities. With an Internet audience of 93 mln people, the Russian emerging market represents the largest online audience in Europe, stimulating substantial e-commerce growth over the last decade. The main objective of this paper is to explore consumer perception of e-commerce adoption factors on two levels — the first are the macro-level factors, associated with the overall environment, institutional factors and trust; the second one is store-level factors, or factors associated with real consumer experiences. This multi-level approach reflects the complexity of consumer thinking about the market — both in terms of the evolving environment, offering opportunities to make decisions and make purchases; and real experience, where the factors are influencing particular consumer decisions and are weighted by consumers as pros and cons. Our study is based on a survey, using a sample of 3 387 respondents representing the consumer perspective. The findings reveal the structure of the driving and limiting factors, highlighting the core role of the trustworthiness and transparency of the e-commerce market players, delivery conditions and store-related risks.
We investigate the evolutionary model with recombination and random switches in the fitness function due to change in a special gene. The dynamical behaviour of the fitness landscape induced by the specific mutations is closely related to the mutator phenomenon, which, together with recombination, plays an important role in modern evolutionary studies. It is of great interest to develop classical quasispecies models towards better compliance with the observation. However, these properties significantly increase the complexity of the mathematical models. In this paper, we consider symmetric fitness landscapes for several different environments, using the Hamilton-Jacobi equation (HJE) method to solve the system of equations at a large genome length limit. The mean fitness and surplus are calculated explicitly for the steady-state, and the relevance of the analytical results is supported by numerical simulation. We consider the most general case of two landscapes with any values of mutation and recombination rates (three independent parameters). The exact solution of evolutionary dynamics is done via a solution of a fourth-order algebraic equation. For the more straightforward case with two independent parameters, we derive the solution using a quadratic algebraic equation. For the simplest case, when there are two landscapes with the same mutation and recombination rates, we derive some effective fitness landscape, mapping the model with recombination to the Crow-Kimura model.
Russia is the largest country in the world, ranking 9th by population with 146.8 million people living on its territory. Russia contains 30% of the world’s natural resources, making it the most resource-rich country in the world. The Russian economy is 6th in the world in terms of GDP (purchasing power parity), according to the IMF. In the Global competitiveness report, Russia ranked 43rd out of 140. Our study of companies’ strategic capabilities is based on a comparative analysis of 5 firms operating in Russia. Three of them are domestic – SIBUR (Siberian-Ural petrochemical and Gas Company), Gazprom Marketing & Trading (part of the Gazprom group), and ByTerg, all representing exporters in the high-tech industry, and 2 multinational companies (MNCs) – Ecolab and Swilar, representing the high-tech and service industries respectively. This qualitative study, relying on semi-structured interviews, revealed that customer orientation is a crucial strategic capability, highlighted by all firms. Very important strategic capabilities also include product manufacturing and general sales capabilities (highlighted by 80% of respondents).
This study presents a snapshot of investment projects in manufacturing that were implemented by foreign investors in Russia during 2017–2018. We assemble a unique database of all new plants opened by foreign companies in Russia during 2012–2018 to clarify the distribution of investment projects implemented during 2017–2018 across industries and territories with different tax regimes. We also identify the most interesting individual investment projects, interrelated investment projects, and elements of collective actions. In general, foreign investors in manufacturing demonstrate high ingenuity in discovering and exploiting the remaining emerging growing market segments and promising niches in consumer and professional markets and express significant persistence in realizing investment projects. We also demonstrate the methods applied to decrease the uncertainty of the project costs by establishing partnerships with local foreign- and domestically owned companies and the attempts to correct the government’s decisions and regulatory measures that are uncomfortable for foreign investors.
Background. Fraud- or theft-related crimes account for the highest number of crimes in the mental health industry in the U.S. Aim. This exploratory study aims to demonstrate a fraudster’s and respective victims’ profiles as well as to identify the loss predictors’ hierarchy in the mental health industry in the U.S. Materials and methods. The Psychiatric Crime database and mixed-effects models are utilized for this purpose. Results. A typical fraudster’s profile is defined as a 53-year old male psychiatrist who victimizes one or two of the largest federal insurance programs in states with high property crime ratios. The results revealed the year and state where the fraud is prosecuted explained the largest portion of the variance in loss size. Predictably, case-specific factors also have a significant impact on the loss. Specifically, Medicaid, the existence of collusion, and fraudster’s age are associated with the fraud loss. Conclusions. This study empirically justifies considering loss, due to healthcare fraud, from a multi-level perspective. Identified typical fraudster’s and respective victim’s profiles helped to elaborate on specific practical recommendations aimed at fraud prevention in the mental healthcare system in the U.S.
This book discusses important topics for engineering and managing software startups, such as how technical and business aspects are related, which complications may arise and how they can be dealt with. It also addresses the use of scientific, engineering, and managerial approaches to successfully develop software products in startup companies. The book covers a wide range of software startup phenomena, and includes the knowledge, skills, and capabilities required for startup product development; team capacity and team roles; technical debt; minimal viable products; startup metrics; common pitfalls and patterns observed; as well as lessons learned from startups in Finland, Norway, Brazil, Russia and USA. All results are based on empirical findings, and the claims are backed by evidence and concrete observations, measurements and experiments from qualitative and quantitative research, as is common in empirical software engineering. The book helps entrepreneurs and practitioners to become aware of various phenomena, challenges, and practices that occur in real-world startups, and provides insights based on sound research methodologies presented in a simple and easy-to-read manner. It also allows students in business and engineering programs to learn about the important engineering concepts and technical building blocks of a software startup. It is also suitable for researchers at different levels in areas such as software and systems engineering, or information systems who are studying advanced topics related to software business.
The article is devoted to a new direction in the field of human resource management - HR-analytics. HR analytics has been actively developing in the past decade as an interdisciplinary field. The leading role in this area is played by information technologies that allow working with big data . However, an analysis of existing approaches to HR analytics shows that it is a kind of a well-known social engineering approach from the beginning of the 20th century, focused on the development and practical application of various kinds of social technologies. Analysis of the stages, methods and approaches used in HR-analytics, gives reason to consider it as a kind of diagnostic social technology. Its development logic in the digital economy requires the use of modern methods of collecting, storing, analyzing heterogeneous and often unstructured data. This allows us to consider HR analytics in the context of management development in a digital economy.