Characteristics of piloting longitudinal birth cohort surveys: a systematic review

Quality & Quantity - This paper aims to provide a comprehensive overview of piloting approaches, systemize piloting processes, and underline good practices in large scale longitudinal birth...     

In search of demanded competencies: designing superior compensation systems

Employees' competencies are decisive components in a firm's competitiveness. Accordingly, the purpose of this paper is to assess whether differences in strategy influence employees' competencies and how the design of compensation systems may best take into account the competencies required by a firm. An analysis was conducted on a sample of manufacturing firms using structural equation models. The findings support the existence of generic competencies, such as results and customer orientation, which are found in both prospector and defender strategic contexts, while other competencies – such as innovation, technical expertise and adaptability – are specific to the prospector strategy. In relation to compensation, the results show that skill-based pay is more strongly associated with competencies such as innovation, technical expertise and adaptability, whereas performance-based compensation systems encourage result-based competency. Therefore, the design of a compensation system should be guided by the competencies required as part of a firm's strategy.     

Organizational dynamics of worker cooperatives in Argentina

Worker cooperatives in Argentina have experienced important changes since the beginning of the nineties and their presence became meaningful from 2003 onwards, due to the stimulation of public policies oriented towards their promotion. Similarly to the context under which they were formed, the unfavourable economic conditions and the worsening of work and life conditions of workers played a major role in the rebirth of the sector. Based on case studies, the article suggests an organizational typology which considers business and associative performance of work cooperatives. The typology accounts for the diverse organizational dynamics and meanings the cooperative model adopts.     

Multiple equilibria in a monopoly market with heterogeneous agents and externalities

We explore the effects of social influence in a simple market model in which a large number of agents face a binary choice: to buy/not to buy a single unit of a product at a price posted by a single seller (monopoly market). We consider the case of positive externalities: an agent is more willing to buy if other agents make the same decision. We consider two special cases of heterogeneity in the individuals' decision rules, corresponding in the literature to the Random Utility Models of Thurstone, and of McFadden and Manski. In the first one the heterogeneity fluctuates with time, leading to a standard model in Physics: the Ising model at finite temperature (known as annealed disorder) in a uniform external field. In the second approach the heterogeneity among agents is fixed; in Physics this is a particular case of the quenched disorder model known as a random field Ising model, at zero temperature. We study analytically the equilibrium properties of the market in the limiting case where each agent is influenced by all the others (the mean field limit), and we illustrate some dynamic properties of these models making use of numerical simulations in an Agent based Computational Economics approach. Considering the optimization of the profit by the seller within the case of fixed heterogeneity with global externality, we exhibit a new regime where, if the mean willingness to pay increases and/or the production costs decrease, the seller's optimal strategy jumps from a solution with a high price and a small number of buyers, to another one with a low price and a large number of buyers. This regime, usually modelled with ad hoc bimodal distributions of the idiosyncratic heterogeneity, arises here for general monomodal distributions if the social influence is strong enough.     

Modelling small‐business credit scoring by using logistic regression,neural networks and decision trees

Previous research on credit scoring that used statistical and intelligent methods was mostly focused on commercial and consumer lending. The main purpose of this paper is to extract important features for credit scoring in small‐business lending on a dataset with specific transitional economic conditions using a relatively small dataset. To do this, we compare the accuracy of the best models extracted by different methodologies, such as logistic regression, neural networks (NNs), and CART decision trees. Four different NN algorithms are tested, including backpropagation, radial basis function network, probabilistic and learning vector quantization, by using the forward nonlinear variable selection strategy. Although the test of differences in proportion and McNemar's test do not show a statistically significant difference in the models tested, the probabilistic NN model produces the highest hit rate and the lowest type I error. According to the measures of association, the best NN model also shows the highest degree of association with the data, and it yields the lowest total relative cost of misclassification for all scenarios examined. The best model extracts a set of important features for small‐business credit scoring for the observed sample, emphasizing credit programme characteristics, as well as entrepreneur's personal and business characteristics as the most important ones. Copyright © 2005 John Wiley & Sons, Ltd.     

The impact of human,social, and psychological capital on academic spin-off internationalization

Journal of International Entrepreneurship - Academic spin-offs (ASOs) are companies with a strong international vocation for two main reasons: first, they market their products and services in...