اکتشاف، استخراج و عقلانیت سازگار: دیدگاه نو شومپیتری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20242||2002||24 صفحه PDF||سفارش دهید||10267 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Simulation Modelling Practice and Theory, Volume 10, Issues 5–7, 15 December 2002, Pages 297–320
Resource allocation between exploration of emerging technological possibilities and exploitation of known technological possibilities involves a delicate trade-off. We develop a model to represent this trade-off under the time-pressing situation where the firm’s existing basis of survival is constantly challenged by competitors’ innovation and imitation. We examine how the employment of an adaptive rule improves a balance between the exploration and the exploitation. Simulation experiments show that an adaptively rational decision rule, or a step-by-step exploration of unknown opportunities based on feedback on returns, is more likely to increase firm survival under diverse conditions than an all-or-nothing approach regarding the unknown opportunities. Furthermore, our study suggests that the adaptively rational rule is self-protected from too much loss, while its potential pay-off can be unbounded above.
The trade-off between exploration of new possibilities and exploitation of known possibilities has been considered a fundamental feature of adaptive systems ,  and . In the management literature, this issue has also received significant attention since March  introduced it. The issue often causes a strategic dilemma in high-tech industries, where the exploration of a new technology puts tension on the resources for developing new products with a known technology. For example, the emergence of a new technology called “combinatorial chemistry” opened up new possibilities in the pharmaceutical industry when it had witnessed a sign of diminishing returns to innovation with the existing technology until the early 1990s.2 The new technology was claimed to dramatically speed up product development cycle. Eli Lilly was among the firms that considered whether to adopt the new technology or not . However, the technology was untried and controversial at the time. Some scientists at Lilly argued that the investment in the new technology would not only delay time to market for products under development but also waste scarce resources, which could be better used with the known technology to enrich the company’s new product pipeline. Others argued that the exploration of combinatorial chemistry would greatly increase Lilly’s R&D productivity, which would become a strategic asset for R&D warfare in the future. The Lilly’s decision problem represents a typical example of the tension between exploration and exploitation, which is tantamount to the problem of deciding to what degree the present should be mortgaged for the future . How should a firm balance the effort between exploration and exploitation? Much of prior research has addressed the difficulty for incumbents to explore a new technology. Theoretical work on learning highlighted the natural tendency that incumbents with the experience in a known technology are inert to or blinded to new technological opportunities (e.g., ,  and ). A popular explanation for this tendency was “competency trap” or “learning myopia”––this is associated with the reduced incentive to learn a new technology as incumbents achieve a high level of efficiency with the existing technology  and . However, this literature has warned managers that incumbents trapped in the existing technological paradigm may have difficulty in surviving later when a new paradigm proves its superiority. Indeed, a number of empirical studies showed that incumbents failed in the face of radical technological change (e.g., , ,  and ). Although much of prior work has contributed to the understanding of the trade-off between exploration and exploitation, scanty attention has been paid to the management issue of how a firm can improve the balance between exploration and exploitation. At least, few formal models exist to address this management issue. The objective of this paper is to shed light on this issue by developing a computational model. Our model incorporates four main assumptions. First, industry competition is characterized by the Schumpeterian creative destruction. In particular, firms compete on development of new products, some of which replace existing ones. Second, like , we assume that firms do not know the consequence of their R&D decision. Third, we assume that a (process) technology shows gradual diminishing returns to its use––the probability of developing a new product decreases with the use of a technology. Finally, we adopt the typical assumption in the learning literature: the firm becomes more efficient up to some point as it accumulates experience with a technology . In this context, a tension comes from the third and the fourth assumptions. The survival pressure may force firms to consider a new technology when an existing technology shows a sign of diminishing returns. Yet, it is unknown whether the new technology offers richer opportunities. Furthermore, firms gained efficiency over time with the existing technology, while it may take time for them to learn the new technology to utilize it for commercial purposes. Here, investment in the new technology necessarily foregoes immediate survival opportunities offered by the existing technology. Since time itself is an important decision variable here, a firm’s choice naturally tends to favor exploitation over exploration as the literature on learning has suggested. Under this time-pressing situation, is there any reasonable adaptive rule with which firms can improve the balance between exploration and exploitation? We constructed an adaptive rule called step-by-step exploration. The rule goes as follows: (1) allocate a small proportion of the R&D expenditure into exploration of the new technology when its potential is unknown; (2) increase or decrease this portion based on its realized return over time. Our simulation results indicate that this step-by-step exploration is more likely to increase firm survival under diverse situations than an all-or-nothing approach. Furthermore, the expected pay-off of the step-by-step exploration is shown to be greater than its expected loss. It is because such an adaptive search protects innovators from too much loss when the new technology offers no or few opportunities. On the other hand, when the technology turns out to be a jackpot, the realized pay-off should be very high.
نتیجه گیری انگلیسی
This paper examined the tension between exploration and exploitation under a situation where a new technology offers poorly understood opportunities while those in an existing technology are not completely exhausted. When innovators’ current basis for survival is constantly challenged by imitative erosion and innovative destruction, innovators tend to pay more attention to immediate survival opportunities by deeply discounting the value of unknown technological opportunities. Yet, our study shows that exploration of new technological possibilities is more likely to improve adaptability for innovators. Full commitment to an emerging technology from the outset, however, may possibly result in the fast decline of the firm or business failure if it offers few opportunities or it is difficult to assimilate. Thus, a step-by-step exploration of new technological possibilities on the basis of feedback on returns is more likely to contribute positively to firm survival. Obviously the model presented here is an oversimplified one, and it says nothing about how firms in reality behave. The results of this study are merely logical consequences of assumptions we made. In particular, our model assumes that firms that stay with the existing technology will not switch to a new technology even when they observe that more new products are drawn from this. Here, adaptability of lagging firms was deliberately controlled in simulation experiments only to achieve our objective of assessing the strategic implications of the two survival strategies; however, some real firms, if not all, may adapt to a changing condition when the market consistently sends a positive signal about a new technology. Another limitation is that like , we side stepped the complexity of demand-side issues. In particular, it appears that the presence of network externalities can exacerbate the strategic dilemma we considered when a new, incompatible technology is introduced to the market. These limitations point to directions for future research, which can add more realistic features and sharpen insights into the balance between exploration and exploitation in a more complex setting.