Model of Motivation for the Top Management of Regional Government Agencies

The purpose of the study is to create a model of motivation for the top management of regional government agencies under which the non-material motivation of top managers will be made dependent on the achieved strategic potential of the region and their material motivation. For this purpose, it is necessary to solve a three-objective problem of global optimisation for the coefficient of natural population growth using a multi-objective genetic algorithm. Each of the three objectives – the strategic potential of the region and the material and non-material motivations of top managers – depends on three factors in the same coordinate system. The first three of the nine factors characterise the system of non-material incentives for top managers in government agencies, the next three refer to the system of their material incentives, and the last three apply to the available strategic potential of the region necessary for its further successful development. The creation of multiple effective solutions using the Pareto front is performed for two primary objectives, namely, the strategic potential of the region and material motivation of top management; then, as a consequence, a set of optimal solutions for non-material motivation is obtained. The conclusion about the actual remuneration (incentives) of the top managers at government agencies in the regions is as follows. For each of the three objectives in a particular region, the latest actual values of the nine factors under study are compared with the nearest planned (optimum) values of the Pareto front. A positive deviation from the optimum is evaluated positively, which makes it possible to additionally incentivise top managers either materially or non-materially

Neural Simulation of Digital Twin of Top Management Motivation Mechanism in Regional Government Agencies

The aim of the research was the problem of neural simulation of the digital twin of non-financial and financial motivation of top management in government agencies, as well as the strategic potential of regions. Bayesian regularization is used as the network training algorithm because the quasi-time series developed for 83 regions in Russia for the period from 2010 to 2021 is highly noisy. The inner layer of the network has 15 neurons since in this case, the network is trained most optimally. In the verification stage of the trained network, the comparison of actual and forecast data showed that in 2021, the error of the trained network was to average the fluctuations of the quasi-time series. In other words, the network does not account for the overall downward trend in the data. This problem requires a separate in-depth study. For instance, in the case of the Nizhny Novgorod Region, it has been observed that in 2020 and 2021, top managers performed better than those in the leading region (Moscow) based on the parameter of the total area of residential premises per capita. Therefore, they should be financially rewarded for their performance. In terms of non-financial motivation, the top managers should be rewarded more in 2021 than in 2020. The strategic potential of the Nizhny Novgorod Region as a whole is more developed in 2021 than in 2020, which allows us to assess the region's development prospects positively

Using a Compound Real Option to Develop an Innovation Strategy for an Industrial Cluster

The purpose of this paper is to apply such a technology to develop an innovation strategy for the pilot clusters defined by the state, which would allow to make flexible management decisions. The proposed method for this involves the use of a compound real option, which includes the following components to be applied in the following order: 1) an option to reduce and exit the cluster strategy, 2) an option to develop and replicate the experience in the cluster, 3) an option to change over and temporarily stop the cluster strategy, and 4) an option to delay the start of the new cluster strategy. Combining a compound real option exactly as presented avoids unreasonable managerial decisions to withdraw from the current cluster strategy, which would include multiple tactical cluster development opportunities that have already been implemented. In other words, a put option is first added to the evaluation of the current strategy. This is an option to reduce and exit the cluster strategy. And then if the current strategy continues, the next three call options are added to it