زمان بندی اکتشاف منابع در رفتار شرکت - روش ابتکاری و شبیه سازی تجربی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20090||2008||8 صفحه PDF||سفارش دهید||4936 کلمه|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 34, Issue 4, May 2008, Pages 2656–2663
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 point of view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an innovative approach using quantum minimization (QM) to tune a composite model comprising 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.
When we think about the firm as a collective of resources (Penrose, 1959), it drives the different aspects of research directions to answer two fundamental strategic questions: the sources of competitive advantage and the growth trajectory of firm. There is fairly general agreement that the accumulation of heterogeneous resources can explain the success of firm for a period of time (Barney, 1986, Barney, 1991, Barney, 1995, Dierickx and Cool, 1989, Peteraf, 1993 and Rumelt, 1984); it also shapes the path of firm’s growth (Penrose, 1959 and Wernerfelt, 1984). However, resources or capabilities, like product, have life cycles (Helfat & Peteraf, 2003). Thus, researchers always remind us the importance of exploring new resources due to the pressure of external changing environment (March, 1991, Penrose, 1959 and Wernerfelt, 1984). We know the importance of resource exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm that are well-explicated in the resources-based view. It is worth understanding that the idea of balance between exploration and exploitation will be solely achieved under the assumption of calculated rationality. However, we in fact do not know when the firm takes it into account and commits itself to the exploring activities. In each occasion of decision-making, decision makers are constrained by bounded rationality (Cyert and March, 1963, Levinthal and March, 1993 and Simon, 1997) which is raised from two facts: (a) managers have limited absorptive capacity and (b) managers acquired finite information subjected to the external changing environment. Therefore, the managers might miss or postpone the exact timing of exploring activities because of their inability on controlling the future uncertainty under the situation of limited rationality. Accordingly, the reinforcement of the precedent tendency mentioned above will be stressed by the conservative personality of managers. All of these will leads the managers to persist on the exploiting the existing resources rather than exploring new ones. So, we concern the timing problem, namely when the risky attitude of managers will be shifted from risk-avoiding exploiting activities to relative risk-taking exploring activities. In the basis of the prospect theory (Kahneman & Tversky, 1979), we argue that the turning point will be triggered by the negative prospects. That is, when the firm is framed by positive performance, it will incline the managers to utilize the existing resources and neglect the need of exploring new ones. On the other hand, when the firm suffers from loss or decline in performance, it will reverse the risky attitude of decision makers to approach risk-taking considerably that will ignite more exploring activities. And then, we can observe that the trajectory of the growth of firm is emerging with the exploration and the following exploitation and so on (Penrose, 1959 and Rothaermel and Deeds, 2004). In the mean time, we also proposed that large firm holds much more resources than small one (Schumpeter, 1934) in this case that leads to the large firm’s ‘value function’ (Kahneman & Tversky, 1979) is flatter than small firm as shown in Fig. 1 where the value function (S-shape) will exhibit different slope between large firm and small firm. Therefore, large firm has revealed low risk-aversion so as to potentially proceed to higher exploring activities in positive frames; in contrast, small firm with the emergence of high risk-seeking in negative frames will then undertake more exploring activities. Full-size image (14 K) Fig. 1. The value function of large firm and small firm. Figure options There are five indicators that are 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. The relationship among these indicators indeed can be used to analyze the timing of resources exploration in the behavior of firm. Several quantitative methods, such linear time series models with single-output, multi-input structure as AR, MA, ARX, ARMA, and ARMAX (Box et al., 1994 and Hamilton, 1994), are applicable to the simulation of the dynamics of the interaction between five indicators. Once a trained structure is built, it interprets the timing of resources exploration in the behavior of firm based on the coefficients with respective to explanatory variables. However, a trained ARMAX (Bowerman & O’Connell, 1993) do not get the least mean-absolute-percent-error for the timing problem. Thus, three remarkable nonlinear models, back-propagation neural network (BPNN) (Haykin, 1999), adaptive support vector regression (ASVR) (Chang, 2005), and quantum minimization (Durr & Hoyer, 2005) tuning ANFIS/NGARCH composite model (Chang, 2006) are also provided in this study so that the performance comparison among methods for the timing problem is compared quantitatively.
نتیجه گیری انگلیسی
The key role of entrepreneurs is in igniting the growth of the firm; however, the entrepreneur’s mindset is unchangeable and always inclines to risk seeking over time due to enjoying the monopoly profits derived from the previous innovation for a long time. If he or she is still aggressive or ambitious, we doubt? In this study, we offer another reason derived from the prospect theory and argue that the exploration is evoked by competing pressures which drive the firm’s profits down and, furthermore, reverse the risk attitudes of decision-makers. Thus, we have proposed innovative approach to the time problem of resources exploration in the behavior of firm in order to provide the vital signal to the administrator in time. The following statements summarize the accomplishment of the proposed methods, including innovative approaches like ANFIS, BPNN, ASVR, and QM-ANFIS/NGARCH. The resulting ARMAX model explains the growth rate of long-term investment, which can help decision-maker to explore new resources due to the pressure of external changing environment. Particularly, the dynamics analyzed in this study can signify the appropriate timing to get the resources exploration started when the risky attitude of managers will be shifted from risk-avoiding to relative risk-taking exploring activities. Clearly, the nonlinear innovative approach QM-ANFIS/NGARCH gets the satisfactory results, and improves the goodness-of-fit better than the linear structure of ARMAX and the nonlinear structure of ANFIS, BPNN, or ASVR. However, the nonlinear innovative models, including ANFIS, BPNN, ASVR, and QM-ANFIS/NGARCH, cannot tell us the exact impact to the growth rate of long-term investment from the other individual factors because those are hidden in the nonlinear system.