Abstract
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.
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References
Nadal, J., Benaul, J.M., Sudría, C.: Atlas de la Industrialización de España, 1750–2000. In: Barcelona, S.L. (ed.) Fundación BBVA. Editorial Crítica (2003)
Cameli, A., Tishler, A.: The Relationships Between Intangible Organizational Elements and Organizational Performance. Strategic Management Journal 25, 1257–1279 (2004)
Camillus, J.C.: Turning Strategy into Action. Tools and Techniques for Implementing Strategic Plans, an APQC Best Practise Research Report. APQC Research Report. Harvard Business Review, New York (2004)
Laguna, M., Martí, R.: Neural Network Prediction in a System for Optimizing Simulations. IEE Transactions (2002)
Sexton, R.S., Alidaee, B., Dorsey, R.E., Johnson, J.D.: Global Optimization for Artificial Neural Networks: A Tabu Search Application. European Journal of Operational Research (1998)
Werbos, P.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis. Harvard, Cambridge (1974)
Parker, D.: Learning Logic, Technical Report TR-87. Center for Computational Research in Economics and Management Science. MIT, Cambridge (1985)
Lecun, Y.: Learning Process in an Asymmetric Threshold Network. In: Disordered Systems and Biological Organization, pp. 233–240. Springer, Berlin (1986)
Rumelhart, D., Hinton, G., Williams, R.: Learning Internal Representations by Error Propagation. In: Rumelhart, D., McCleeland, J. (eds.) Parallel Distributed Processing, Explorations in the Microstructure of Cognition, vol. 1, MIT Press, Cambridge (1986)
Montana, D.J., Davis, L.: Training Feedforward Neural Networks Using Genetic Algorithms. In: Kaufmann, M. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, San Mateo, CA, pp. 379–384 (1989)
Schaffer, J.D.: Combinations of Genetic Algorithms with Neural Networks or Fuzzy Systems. In: Zurada, J.M., Marks, R.J., Robinson, C.J. (eds.) Computational Intelligence: Imitating Life, pp. 371–382. IEEE Press, Los Alamitos (1994)
Schaffer, J.D., Whitley, D., Eshelman, L.J.: Combinations of Genetic Algorithms and Neural Networks: A survey of the State of the Art. In: COGANN 1992 Combinations of Genetic Algorithms and Neural Networks, pp. 1–37. IEEE Computer Society Press, Los Alamitos (1992)
Dorsey, R.E., Johnson, J.D., Mayer, W.J.: A Genetic Algorithm for the Training of Feedforward Neural Networks. In: Johnson, J.D., Whinston, A.B. (eds.) Advances in Artificial Intelligence in Economics, Finance and Management, vol. 1, pp. 93–111. JAI Press, Greenwich (1994)
Topchy, A.P., Lebedko, O.A., Miagkikh, V.V.: Fast Learning in Multilayered Networks by Means of Hybrid Evolutionary and Gradient Algorithms. In: Proceedings of International Conference on Evolutionary Computation and its Applications (1996)
Sexton, R.S., Dorsey, R.E., Johnson, J.D.: Optimization of Neural Networks: A Comparative Analysis of the Genetic Algorithm and Simulated Annealing. European Journal of Operational Research 114, 589–601 (1999)
Aragon, A., Krasnogor, N., Pacheco, J.: Memetic Algorithms. In: Marti, R., Alba, E. (eds.) Metaheuristics in Neural Networks Learning. Kluwer, Dordrecht (2006)
De Treville, S.: Disruption, Learning and System Improvement in JIT Manufacturing. Thesis GSB. Harvard University, Ann Arbor (1987)
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Palma, A.M.L., Bárcena, L.S., Pacheco, J. (2006). A Closed Model for Measuring Intangible Assets: A New Dimension of Profitability Applying Neural Networks. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_98
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DOI: https://doi.org/10.1007/11875581_98
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