2017. No. 4 (42)

Tiare-Maria Brasseur - Lecturer, Department of e-Business, University of Vienna
Address: 1, Oskar-Morgenstern-Platz, Vienna, 1090, Austria
E-mail: tiare.brasseur@gmail.com

Andreas Mladenow - Lecturer, Department of e-Business, University of Vienna
Address: 1, Oskar-Morgenstern-Platz, Vienna, 1090, Austria
E-mail: andreas.mladenow@univie.ac.at

Christine Strauss - Professor, Department of e-Business, University of Vienna
Address: 1, Oskar-Morgenstern-Platz, Vienna, 1090, Austria
E-mail: christine.strauss@univie.ac.at

      In today’s fast-paced business environment, firms are constantly pressured to innovate in order to remain competitive. Business model innovation (BMI) has recently attracted increasing attention as a promising approach to achieve competitive advantage in the face of fierce competition. Despite its great potential, however, BMI also entails high degrees of complexity, uncertainty and financial risk. Fueled by the rise of digital technologies, BMI has become increasingly open and collaborative in the recent past.
      The aim of this paper is to investigate the role and implications of open and collaborative practices in BMI and to provide a comprehensive review of available literature in this field. Therefore, a systematic review of literature at the intersection of Open Innovation (OI) and BMI has been carried out. Our analysis of the literature identified two major research streams in open business model innovation (OBMI): OBMI trends (customer-driven BMI, BM co-creation, early BM validation, virtual collaboration, design thinking) and OBMI effects. Overall, the findings support a growing trend of collaboration and co-creation in BMI supported by digital or tangible tools, and further reveal that OI has a direct positive effect on BMI success. Analysis of the literature also shows that the field of OBMI is still an under-researched area.

Yury M. Akatkin - Head of Laboratory of Social-Demographic Statistics, Plekhanov Russian University of Economics
Address: 36, Stremyanny Lane, Moscow, 117997, Russian Federation
E-mail: u.akatkin@semanticpro.org

Oleg E. Karpov - Corresponding Member, Russian Academy of Sciences; General Director of Pirogov National Medical-Surgical Center
Address: 70, Nizhnyaya Pervomaiskaya Street, Moscow, 105203, Russian Federation
E-mail: nmhc@mail.ru

Valery A. Konyavskiy - Head of Information Security Department, Moscow Institute of Physics and Technology
Address: 1A, Kerchenskaya Street, Moscow, 117303, Russian Federation
E-mail: konyavskiy@gospochta.ru

Elena D. Yasinovskaya - Senior Researcher, Laboratory of Social-Demographic Statistics, Plekhanov Russian University of Economics
Address: 36, Stremyanny Lane, Moscow, 117997, Russian Federation
E-mail: elena@semanticpro.org

      The main objective of digital transformation is to fulfill the needs of a “new digital generation customer” for on-demand delivery, quality and personalization. ”Anything as a service” has become the key principle of the digital paradigm. This is about a data-oriented service which relies on sharing information resources (including public ones) and the requirements for interoperability, security and trust. This paper presents the main approaches to digital transformation based on the example of the most innovatively active sectors such as banking and healthcare. We compare the proprietary development of digital services (products) to the building of a digital sector ecosystem aimed at attracting an unlimited number of participants. We defined the purpose of creating an ecosystem that is to provide the population with digital services formed on demand, in real time, in compliance with legislation and regulations, as well as in the context of maximum trust. We emphasize the role of openness for uniting the efforts of the community interested in the development of a digital industry, extension of public-private partnerships and building a competitive environment in order to ensure the rapid growth of available digital services, as well as to improve their quality. Since the knowledge economy is the basis for the digital economy, the authors consider it especially important to form a semantic core which acts as the carrier of knowledge in a digital sector ecosystem. We confirmed the necessity to implement the semantic core by a brief analysis of modern semantic approaches to standardization of information sharing in the above-mentioned industries, such as FIBO, BIAN (banking), HL7 and UMLS (health). The research carried out allowed the authors to design the conceptual architecture of the ecosystem and to suggest several proposals for digital transformation of an industry. The proposals express the necessity of state support for innovation and providing the conditions for the entry of new digital products based on the following principles: accessibility, timeliness, personalization, adaptability and security.

Zinaida K. Avdeeva - Senior Researcher, Trapeznikov Institute of Control Sciences; Associate Professor, Department of Innovation and Business in Information Technologies, National Research University Higher School of Economics
Address: 65, Profsoyuznaya Street, Moscow, 117997, Russian Federation
E-mail: avdeeva@hse.ru

Alexander A. Utrobin - Bachelor in Business Informatics, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: alexanderutrobin11@gmail.com

Ilya Y. Lykov - MSc Program Student, Faculty of World Economy and International Affairs, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: lykov.ilya@gmail.com

      Currently, tender procedures occupy an important place in the work of the procurement department of any company. Most of the modern recommendation services operate on web-platforms. Implementation of a tendering system in a company can increase the level of maturity of the procurement process and will not require serious changes in the structure of the processes. This article is devoted to the study of the structure of an integrated tender recommendation system. The integrated tender recommendation system is based on procedures prescribed by federal laws, aggregated tenders from different e-trade sites (presenting state and commercial platforms); it offers to its users additional services.
     The main purpose of the study is to develop an effective model of an integrated tender recommendation system. In the description part of this article, we present information on peculiarities of the tender procedure in the Russian Federation and modern advisory services are considered. The functional advantages of an integrated system in comparison with the web platform are set out. The structure of the system is designed using several approaches. Using the IDEF0 methodology, a functional model of the system that reflects the work of processes has been developed and described. The operation of the main system and subsystems has been analyzed using the projected diagrams of the DFD methodology. A mathematical model of dynamic filtering of tenders for creating recommendations to users is described. Relying on the basic principles of collaborative filtering and, with the help of appropriate algorithms, the integrated system gives recommendations to users and determines the probability of success in a particular tender. Application of such technology of tenders is possible in companies of different scale. The developed structure of the integrated system and filtering methods for recommendations are based on the basic principles of a new international trend – e-tendering.

Vyacheslav I. Zhukov - Assistant Professor, Department of Innovation and Business in Information Technologies, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: vzhukov@hse.ru

Mikhail M. Komarov - Associate Professor, Department of Innovation and Business in Information Technologies, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: mkomarov@hse.ru

      In modern e-business, there are many ways to attract potential customers to the site both with the help of offline and online methods. Companies usually use several channels to attract customers, differing in the placement of advertising, payment models and other parameters.
      One of the most popular online methods is using CPA networks, which allow webmasters to place on their websites links to the advertised website and earn rewards for customers who purchased a service by clicking on the link. CPA networks work on the basis of payment for achieving targeted goals. A targeted goal can occur both online and offline. The most important task is to link the source of attraction (usually certain UTM tags) to the target goal of the client, since remuneration for the CPA network should only occur for orders from customers who are drawn to the CPA network. There are problems fixing operations, linking to source of attraction, storing and providing access to these data.
      In this paper, we give a brief overview of various approaches to solving the problem: log analysis, use of marketing pixels and web analytics tools. We have analyzed the benefits and challenges of these methods, which were given to solve the task of fixing the target actions of clients and provided access to the data for the CPA network. Also in this article, we have described a practical case of integration with the CPA network based on the use of end-to-end web analytics. The advantages, disadvantages and limitations of the proposed method are set out in this paper.

Zhanna N. Zenkova - Associate Professor, Institute of Applied Mathematics and Computer Science, National Research Tomsk State University; SRO “Association of professional actuaries”
Address: 36, Lenin Street, Tomsk, 63403047, Russian Federation  
E-mail: zhanna.zenkova@mail.tsu.ru

Elizaveta A. Krainova - Doctoral Student, Institute of Applied Mathematics and Computer Science, National Research Tomsk State University;
Address: 36, Lenin Street, Tomsk, 63403047, Russian Federation  
E-mail: lanshakoval@gmail.com

      In this paper, the task of increasing the accuracy of net premium estimations in non-life insurance is considered. Improvements are achieved by involving additional information about a known q-quantile of loss cumulative distribution function. The additional information is used by projection the empirical cumulative distribution function onto the class of cumulative distribution functions with a certain q-quantile, and then the modified empirical cumulative distribution function is substituted into the integral that yields the mean value. This allows us to obtain a modified estimation of mean value using additional information about the q-quantile which is unbiased and its variance is asymptotically less than the variance of the classical sample mean, so that the mean-square error of the modification is also smaller. Therefore, the modified estimation is more accurate than the classical one for a large sample size.
      The influence of a quantile value on the variance of the new estimation is studied for uniform, triangular and normal distributions. It is suggested that the minimum of the variance is reached when a known quantile is equal to the median (symmetry center) for symmetrical distribution. Based on Simpson triangular distribution, it was shown that for cases of skewed distributions involving the quantile allows one to decrease the variance more significantly than for symmetrical ones.
      The modified estimation of mean value is applied to a real data set for calculation of a net premium. The data contain information about payments for voluntary health insurance of some insurance company. It is demonstrated that the classical method underestimates the net premium, and so it could lead to the company’s bankruptcy. After applying the new modified technique, the net premium becomes higher and the bankruptcy risk is reduced as well.
      This paper contains practically significant results which make it possible to give important recommendations to an insurance company.

Sofiya Y. Lobanova - BSc Program Student, Tikhonov Moscow Institute of Electronics and Mathematics, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: s.lobanova@usabilitylab.net

Alexander A. Chepovskiy - Associate Professor, School of Applied Mathematics, Tikhonov Moscow Institute of Electronics and Mathematics, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: aachepovsky@hse.ru

      In this paper, we propose and implement a method for detecting intersecting and nested communities in graphs of interacting objects of different natures. For this, two classical algorithms are taken: a hierarchical agglomerate and one based on the search for k-cliques. The combined algorithm presented is based on their consistent application. In addition, parametric options are developed that are responsible for actions with communities whose sizes are smaller than the given k, and also with single vertices. Varying these parameters allows us to take into account differences in the topology of the original graph and thus to correct the algorithm.
      The testing was carried out on real data, including on a group of graphs of a social network, and the qualitative content of the resulting partition was investigated. To assess the differences between the integrated method and the classical algorithms of community detections, a common measure of similarity was used. As a result, it is clearly shown that the resulting partitions are significantly different. We found that for the approach proposed in the article the index of the numerical characteristic of the partitioning accuracy, modularity, can be lower than the corresponding value for other approaches. At the same time, the result of an integrated method is often more informative due to intersections and nested community structure.
      A visualization of the partition obtained for one of the examples by an integrated method at the first and last steps is presented. Along with the successfully found set of parameters of the integrated method for small communities and cut off vertices in the case of social networks, some shortcomings of the proposed model are noted. Proposals are made to develop this approach by using a set of parametric algorithms.

This work was supported by the Russian Foundation for Basic Research (projects No. №16-29-09546 and №16-07-00641) 

Alexander G. Madera - Professor, Department of Mathematics of the Faculty of Economics, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: amadera@hse.ru

      This paper is devoted to mathematical modeling and optimization of business processes and process systems under conditions of uncertainty. At present, modeling of business processes is mainly descriptive, which does not allow quantitative modeling and optimization in the design of processes and process systems. In addition, the existing methods of decision-making in business processes are based on the assumption that the decisive factors are deterministic. Despite uncertainty of the real processes caused by the uncertainty of future costs of resources, the market environment, economy, finances, etc,, the factors of an uncertain future are either not taken into account, or are believed to be the same as those observed  currently.
      In this paper, a stochastic interval mathematical optimization model is developed. This model allows us to simulate in a quantitative way the business processes and process systems in which they take place, taking into account the uncertainties of the future state of the economy, finances, market environment, costs of resources, as well as future realization of chances and risks related to the productive, supporting, and service processes. The criterion for optimality of the model is the maximization of the smallest deviation of the projected chances and risks, which makes it possible to make the best decision in the case that the most unfavorable conditions for the business process occur in the future. The criterion of optimality adopted in the mathematical model takes into account not only the uncertainty of the future state of the economy, finance, and market environment, but also the psychology of decision-making and the subjective nature of  judgments and estimates. We present a concept and method for estimating the inductive (logical, subjective) probabilities of the occurrence of uncertain predicted business process factors.
      The models and methods developed in the paper make it possible to carry out mathematical modeling and optimization of business processes in a variety of activities without restrictions on the complexity of the structural model of the business process, the qualitative and quantitative composition of the connections in the process systems. On their basis, a software package for the quantitative design of business processes and process systems under conditions of uncertainty can be developed.