Studying the dynamics of nonlinear interaction between enterprise populations

The BCG Matrix was introduced almost 50 years ago, and is today considered one of the most iconic strategic planning techniques. Using management fashion theory as a theoretical lens, this paper examines the historical rise, fall and persistence of the BCG Matrix. The analysis highlights the role played by fashion-setting actors (e.g., consultants, business schools and business media) in the rise of the BCG Matrix. However, over time, portfolio planning models such as the BCG Matrix were attacked and discredited by a host of different actors, and gradually fell out of favor. Even though the BCG Matrix has fallen from grace, it is still alive and has left an imprint on management education and practice. Despite being largely discredited in academic circles, many practitioners still view it as an important corporate portfolio planning technique.

With the development of the information society, there has been rapidly growing number of international research on the role of various (information, innovation, intelligent) technologies as a catalyst to fight corruption. As you know, the problems posed by corruption are economic, social and political consequences. In recent years economists and various researchers have shown increasing interest in studying the phenomenon of corruption. Many researchers in the field of studying corruption phenomenon are of theoretical nature, which studied different behaviors without having developed effective methods and technologies to identify and prevent corruption in the various spheres of government. In this regard, there is a need to study the world experience of application of technologies in the prevention of corruption and anti-corruption platform in Ukraine. This will allow to adapt them to positive experience in the implementation of mechanisms to identify and prevent corruption in Ukraine. The authors believe that to obtain a more accurate picture of the corruption situations that provide characteristics and its quantitative description is possible only by means of modelling the corruption phenomenon. In this regard, the analysis of theoretical models of corruption was made: the modelling made by the mathematical notation of the models and conclusions on the effectiveness of their application in management. Using simulation, the authors came to the conclusion that the detection and prevention of corruption should be carried out with the use of technologies and the system approach: from the study of statistics and use of anti-corruption platforms, public registers and databases to logical and probabilistic (LP) risk models of corruption.

To improve the marketing activity and brand management and justify the most effective marketing decisions, organizations should implement different information technologies, mathematical methods and models into the marketing decision support system (MDSS). The goal of this paper is to form an architecture of an MDSS, the model base of which is developed on Data Science tools, in particular regression analysis and machine learning methods. The proposed MDSS is a multi-agent information system comprising nine intellectual agents (market environment monitoring, data processing, marketing mix modeling, price policy support, portfolio management, strategic analysis, forecasting, customer segmentation, and customer classification). The functionality of these agents is realized through Data Science, which allows for the optimization of marketing activities (e.g., an effective brand management strategy and its elements (portfolio strategy, price policy, and media strategy) or solving the problems of attracting new and retaining current customers with the maximal return on marketing investments). The MDSS analyzes the marketing environment, media activity, and business indicators by constructing different models and forecasting various combinations of marketing factors to select the best one. The joint work of MDSS agents provides decision-makers with interactive reports. The research findings offer a scientific basis for making effective marketing decisions based on data, and the proposed MDSS can become part of an intelligent system for planning marketing activities.