Becoming The Boss: Discretion And Postsuccession Success In Family Firms

Family firms can enjoy substantial longevity. Ironically, however, they are often imperiled by the very process that is essential to this longevity. Using the concept of managerial discretion as a starting point, we use a human agency lens to introduce the construct of successor discretion as a factor that affects the family business succession process. While important in general, successor discretion is positioned as a particularly relevant factor for productively managing organizational renewal in family businesses. This study represents a foundation for future empirical research investigating the role of agency in entrepreneurial action in the family business context, which consequently can contribute to the larger research literature on succession and change

Comparison between Spanish and Italian regulation on cooperative firms: traditional or hybrid model?

This paper analyses the differences between the regulations on agrarian cooperatives set up in Spain and Italy. Some of these aspects are related to the solutions proposed and used by cooperatives belonging to the hybrid model to solve problems faced by the cooperatives belonging to the traditional model. The main differences in the calculation of returns and reserves and in the fiscal aspects involved have also been analysed. Although in both countries there exist regulations referring to the hybrid model, the lack of conditions required for their implementation and the absence of professional management makes cooperatives of both countries fall within the traditional model. Several differences have been found in terms of the calculation of returns and reserves and of the fiscal aspects involved

Brain status data analysis by sliding EMD

Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes. The method is fully adaptive and generates a complete set of orthogonal basis functions, called Intrinsic Mode Functions (IMFs), in a purely data-driven manner. Amplitude and frequency of IMFs may vary over time which renders them different from conventional basis systems and ideally suited to study non-linear and non-stationary time series. However, biomedical time series are often recorded over long time periods. This generates the need for efficient EMD algorithms which can analyze the data in real time. No such algorithms yet exist which are robust, efficient and easy to implement. The contribution shortly reviews the technique of EMD and related algorithms and develops an on-line variant, called slidingEMD, which is shown to perform well on large scale biomedical time series recorded during neuromonitoring

Optimizing blind source separation with guided genetic algorithms

This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its application to the problem of blind source separation (BSS) on post-nonlinear mixtures. The paper also includes a formal proof on the convergence of the proposed algorithm using guiding operators, a new concept in the GA scenario. This approach is very useful in many fields such as forecasting indexes in financial stock markets, where the search for independent components is the major task to include exogenous information into the learning machine; or biomedical applications which usually use a high number of input signals. The guiding GA (GGA) presented in this work, is able to extract IC with faster rate than the previous ICA algorithms, as input space dimension increases. It shows significant accuracy and robustness than the previous approaches in any case. In addition, we present a simple though effective contrast function which evaluates individuals of each population (candidate solutions) based (a) on estimating the probability densities of the outputs through histogram approximation and (b) evaluating higher-order statistics of the outputs

Hybridizing sparse component analysis with genetic algorithms for microarray analysis

Nonnegative matrix factorization (NMF) has proven to be a useful tool for the previous termanalysisnext term of nonnegative multivariate data. However, it is known not to lead to unique results when applied to blind source separation (BSS) problems. In this paper we present an extension of NMF capable of solving the BSS problem when the underlying sources are sufficiently previous termsparse.next term In contrast to most well-established BSS methods, the devised previous termalgorithmnext term is capable of solving the BSS problem in cases where the underlying sources are not independent or uncorrelated. As the proposed fitness function is discontinuous and possesses many local minima, we use a previous termgenetic algorithmnext term for its minimization. Finally, we apply the devised previous termalgorithmnext term to real world previous termmicroarraynext term data