A Closed Model for Measuring Intangible Assets: A New Dimension of Profitability Applying Neural Networks

The definition of a model should contain something more than purely conceptual development. Its discriminatory characteristics should harbour, in practice, the intention to uncover unknown opportunities in times of globalization. Quantification of the intangible value of the service sector must become another management strategy; thereby consolidating the wealth of each company and gearing up -just like in a mechanism- the variables that can predict the value of these environments which are abound in opportunities, something that has been hardly considered until lately. The rest of this paper deals with the development of M6PROK (Model of the Six Profitability Stages of Knowledge) using an artificial neural architecture. M6PROK is a mirror in which companies can look at themselves and whose reflection should provide a basis for the solution of issues concerning the profitability that knowledge brings about and the awareness of this, as well as supporting decision-making processes to consolidate business strategies.

[1]  Bahram Alidaee,et al.  Global optimization for artificial neural networks: A tabu search application , 1998, Eur. J. Oper. Res..

[2]  Randall S. Sexton,et al.  Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing , 1999, Eur. J. Oper. Res..

[3]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[4]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[5]  Ken Irons Turning Strategy into Action , 1991 .

[6]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[7]  Rafael Martí,et al.  Neural network prediction in a system for optimizing simulations , 2002 .

[8]  Y. L. Cun Learning Process in an Asymmetric Threshold Network , 1986 .

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  A. Carmeli,et al.  The relationships between intangible organizational elements and organizational performance , 2004 .

[11]  J. D. Schaffer,et al.  Combinations of genetic algorithms and neural networks: a survey of the state of the art , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[12]  Alexander P. Topchy,et al.  FAST LEARNING IN MULTILAYERED NEURAL NETWORKS BY MEANS OF HYBRID EVOLUTIONARY AND GRADIENT ALGORITHMS , 2007 .

[13]  Yann LeCun,et al.  Learning processes in an asymmetric threshold network , 1986 .