Intelligence-Based Model to Timing Problem of Resources Exploration in the Behavior of Firm
We have insight into the importance of resource exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the resources-based view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an intelligence-based model using quantum minimization (QM) to tune a composite model of adaptive neuron-fuzzy inference system (ANFIS) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) such that it constitutes the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In particularly, this proposed approach outperforms several typical methods such as auto-regressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or adaptive support vector regression (ASVR) for this timing problem in term of comparing their achievement and the goodness of fit. Consequently, the preceding methods involved in this problem truly explain the timing of resources exploration in the behavior of firm. Meanwhile, the performance summary among methods is compared quantitatively.
KeywordsMean Absolute Percent Error Timing Problem Mean Absolute Deviation Strategic Management Journal Common Equity
Unable to display preview. Download preview PDF.
- 1.Penrose, E.: The Theory of the Growth of the Firm. Oxford University Press, Oxford (1959)Google Scholar
- 2.Rumelt, R.P.: Toward a Strategic Theory of the Firm. In: Lamb, R.B. (ed.) Competitive Strategic Management, pp. 556–570. Prentice-Hall, Englewood Cliffs (1984)Google Scholar
- 5.Barney, J.B.: Looking Inside for Competitive Advantage. Academy of Management Executive 9(4), 49–61 (1995)Google Scholar
- 11.Cyert, R.M., March, J.G.: A Behavioral Theory of the Firm. Prentice-Hall, New Jersey (1963)Google Scholar
- 13.Simon, H.A.: Administrative Behavior: A Study of Decision-Making Process in Administrative Organization. Free Press, New York (1997)Google Scholar
- 14.Kahneman, D., Tversky, A.: Prospect Theory: An analysis of Decision Under Risk. Econometrica, 263–291 (1979)Google Scholar
- 16.Schumpeter, J.A.: The Theory of Economic Development. Transaction Publishers, New Brunswick (1934)Google Scholar
- 23.Durr, C., Hoyer, P.: A Quantum Algorithm for Finding the Minimum, http://arxiv.org/abs/quant-ph/9607014
- 26.Boyer, M., Brassard, G., Hoyer, P., Tapp, A.: Tight Bounds on Quantum Searching. Fortschritte Der Physik (1998)Google Scholar
- 27.Grover, L.K.: A Fast Quantum Mechanical Algorithm for Database Search. In: Proc. 28th Ann. ACM Symp. Theory of Comp., pp. 212–219. ACM Press, New York (1996)Google Scholar
- 28.TEJ database, Taiwan Economic Journal Co. Ltd.,Taiwan (2004), http://www.tej.com.tw/
- 29.Diebold, F.X.P.: Elements of Forecasting. South-Western, Cincinnati (1998)Google Scholar