Organizational resources and intrapreneurial activities: a cross-country study

The importance of entrepreneurship for social and economic growth is generally accepted. In addition, intrapreneurship or corporate entrepreneurship is recognized as one of the key elements for organizational development. In this context, corporate culture and, specifically, entrepreneurial competences are considered to be catalysts for intrapreneurship. The main purpose of this paper is to analyse the influence of resources and capabilities on the probability of becoming an intrapreneur. Using data obtained from the Global Entrepreneurship Monitor (GEM) for 39 countries, and a logistic model, the study shows that entrepreneurial resources and capabilities, such as previous entrepreneurial experience, entrepreneurial competences and the ability to detect business opportunities, influence intrapreneurial behaviour. The contributions of this research are both conceptual (advancing corporate entrepreneurship theory) and practical (relating to the design of policies to foster intrapreneurial activities).

Recovery Risk and Labor Costs in Public-Private Partnerships : Contractual Choice in the US Water industry

We use an ordered logistic model to empirically examine the factors that explain varying degrees of private involvement in the U.S. water sector through public-private partnerships. Our estimates suggest that a variety of factors help explain greater private participation in this sector. We find that the risk to private participants regarding cost recovery is an important driver of private participation. The relative cost of labor is also a key factor in determining the degree of private involvement in the contract choice. When public wages are high relative to private wages, private participation is viewed as a source of cost savings. We thus find two main drivers of greater private involvement: one encouraging private participation by reducing risk, and another encouraging government to seek out private participation in lowering costs.

Adapting football to the child: an application of the logistic regression model in observational mewthodology.

Logistic regression is included into the analysis techniques which are valid for observationalmethodology. However, its presence at the heart of thismethodology, and more specifically in physical activity and sports studies, is scarce. With a view to highlighting the possibilities this technique offers within the scope of observational methodology applied to physical activity and sports, an application of the logistic regression model is presented. The model is applied in the context of an observational design which aims to determine, from the analysis of use of the playing area, which football discipline (7 a side football, 9 a side football or 11 a side football) is best adapted to the child"s possibilities. A multiple logistic regression model can provide an effective prognosis regarding the probability of a move being successful (reaching the opposing goal area) depending on the sector in which the move commenced and the football discipline which is being played.

Inventory-routing model, for a multi-period problem with stochastic and deterministic demand

The need for integration in the supply chain management leads us to considerthe coordination of two logistic planning functions: transportation andinventory. The coordination of these activities can be an extremely importantsource of competitive advantage in the supply chain management. The battle forcost reduction can pass through the equilibrium of transportation versusinventory managing costs. In this work, we study the specific case of aninventory-routing problem for a week planning period with different types ofdemand. A heuristic methodology, based on the Iterated Local Search, isproposed to solve the Multi-Period Inventory Routing Problem with stochasticand deterministic demand.

Validation procedures in radiological diagnostic models. Neural network and logistic regression

The objective of this paper is to compare the performance of twopredictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods. One hundred and sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross validation, leave-one-out and three different bootstrap algorithms. The final results of each model were compared with error rate and the area under receiver operating characteristic curves (Az). The neural network obtained statistically higher Az than LR with cross validation. The remaining resampling validation methods did not reveal statistically significant differences between LR and NN rules. The neural network classifier performs better than the one based on logistic regression. This advantage is well detected by three-fold cross-validation, but remains unnoticed when leave-one-out or bootstrap algorithms are used.

A recoverable production planning model

Aware of the importance of developing new alternatives to improve the performance of the companies, our purpose in this paper is to develop a medium term production planning model that deals with the concepts of Partnership and Reverse Logistics. Our model takes advantage of the synergies of integration, developing a model for global production planning that generates the optimal production and purchasing schedule for all the companies integrating a logistic chain. In a second part of the paper we incorporate products returns to the first model proposed, and analyze the implications they have over this model. We use some examples with different configurations of supply chains varying the number of production plants, distribution centers and recovery plants. To solve the model we have combined optimization and simulation procedures.

A Hierarchical Approach for Multi-task Logistic Regression

Peer-reviewed