The Effects of Self-Reinforcing Mechanisms on Firm Performance

This study empirically investigates the influence of the market-bound (i.e., interaction and network effects) on the firm-bound (i.e., scale and learning effects) self-reinforcing mechanisms, and their combined effect on product and organizational performance. The findings from a sample of 257 manufacturing firms reveal that interaction effects have a positive effect on network effects. Network effects have a positive impact on the potential for firms to realize scale and learning effects, which in turn, is positively related to their actual realization of these effects. The actual realization of scale and learning effects has a positive effect on product performance, which in turn positively influences organizational performance. These effects are robust across industries and provide ample opportunities for future research.management;economics;increasing returns;self-reinforcing mechanisms

A Managerial Perspective on the Logic of Increasing Returns

The focus of this study is on the challenges faced by managers in effectively dealing with the new management logic of increasing returns as the information and knowledge intensity of their transformation processes rises. Dealing with these challenges is especially relevant for companies currently making the transition from capital and physical labor intensive transformation processes (old economy) to information and knowledge intensive transformation processes (new economy). For these companies, this study provides a definition of increasing returns, explains the sources of increasing returns and develops tools for monitoring the realization of increasing returns. These tools are applied at the Randstad Group, a leading temporary employment agency based in the Netherlands, currently making the transition from the old to the new economy. The study concludes with a discussion of the management implications of the new logic of increasing returns and suggestions for further research.management;economics;interaction effects;increasing returns;network effects

Statistical Software for State Space Methods

In this paper we review the state space approach to time series analysis and establish the notation that is adopted in this special volume of the Journal of Statistical Software. We first provide some background on the history of state space methods for the analysis of time series. This is followed by a concise overview of linear Gaussian state space analysis including the modelling framework and appropriate estimation methods. We discuss the important class of unobserved component models which incorporate a trend, a seasonal, a cycle, and fixed explanatory and intervention variables for the univariate and multivariate analysis of time series. We continue the discussion by presenting methods for the computation of different estimates for the unobserved state vector: filtering, prediction, and smoothing. Estimation approaches for the other parameters in the model are also considered. Next, we discuss how the estimation procedures can be used for constructing confidence intervals, detecting outlier observations and structural breaks, and testing model assumptions of residual independence, homoscedasticity, and normality. We then show how ARIMA and ARIMA components models fit in the state space framework to time series analysis. We also provide a basic introduction for non-Gaussian state space models. Finally, we present an overview of the software tools currently available for the analysis of time series with state space methods as they are discussed in the other contributions to this special volume.

The Effects of Self-Reinforcing Mechanisms on Firm Performance

This study empirically investigates the influence of the market-bound (i.e., interaction and network effects) on the firm-bound (i.e., scale and learning effects) self-r

A Managerial Perspective on the Logic of Increasing Returns

The focus of this study is on the challenges faced by managers in effectively dealing with the new management logic of increasing returns as the information and knowledge intensi

How to Determine the Increasing Returns Sensitivity of Your Industry?

Increasing returns means that self-reinforcing mechanisms are at work within firms and markets. These mechanisms come in four forms: scale effects, learning effects, network effects and social interaction effects. Some industries are more sensitive to increasing returns than others. It is important that managers are able to assess the increasing returns sensitivity of their industry. Therefore we have developed an analytical tool that allows managers to assess their industry’s sensitivity to increasing returns. Four case studies are used to illustrate this typology. The analytic tool shows that an industry has high increasing returns sensitivity if a combination of the following situations exists: 1) high fixed costs and low, or even zero, variable costs, indicating a high sensitivity to scale effects, 2) a high level of complexity of the business process and/or the products, indicating a high sensitivity to learning effects, 3) low product utility and high network utility, indicating a high sensitivity to network effects and finally, 4) a high degree of social involvement by customers and potential customers, indicating a high sensitivity to social interaction effects

Разработка алгоритма верификации параметров линии электропередачи в расчетных моделях и его программная реализация

Объектом исследования являются методы верификации параметров ЛЭП в математических моделях для расчетов установившихся режимов электроэнергетических систем и их программная реализация. В ходе разработки приложения использовались стандартные библиотеки, а также библиотеки, находящиеся в открытом доступе. Для взаимодействия с ОИК СК-2007 использовались стандартные инструменты для доступа к данным. В ходе разработки алгоритмов использовались отечественные и зарубежные методики определения параметров линии электропередачи, алгоритм учитывает требования к созданию и актуализации расчетной модели для расчетов установившихся режимов.The objects of study are methods for verifying the parameters of power lines in mathematical models for calculations for the establishment of energy systems and their software implementation. During the development of the application, standard libraries were used, as well as libraries that are in the public domain. To interact with the OIC SK-2007, standard tools for accessing data were used. During the development of the algorithms, national and foreign methods for determining the parameters of power lines were used, the algorithm takes into account the requirements for the development and updating of calculations for models established for calculations