Intelligence-Based Model to Timing Problem of Resources Exploration in the Behavior of Firm

Abstract

AbstractWe 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 ErrorTiming ProblemMean Absolute DeviationStrategic Management JournalCommon EquityThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.