اکتشاف و بهره برداری دوباره: توسعه مدل یادگیری متقابل مارس
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20051||2005||22 صفحه PDF||سفارش دهید||9372 کلمه|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Scandinavian Journal of Management, Volume 21, Issue 4, December 2005, Pages 407–428
A system of actors, appropriately organized, is able to learn even in situations where individuals in isolation cannot. This was one of the most important, though seldom emphasized, insights of March's paper [March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87]. The present paper builds on March's original simulation and incorporates a number of different real-world organizational features. The results suggest that unconstrained experimentation is of great benefit to organizational learning, although it should not be carried to excess. Low levels of turnover in personnel are beneficial and mitigate the problem of high socialization March noted in 1991. Inclusion in the policy-making elite should be predicated on performance rather than seniority and on shorter rather than longer individual performance histories, particularly when environments are changing rapidly. Finally, erring on the side of stringency in selecting members of the organization for the policy-making elite is better than erring toward laxity.
An important albeit seldom emphasized aspect of March's (1991) paper, “Exploration and Exploitation in Organizational Learning”, is that individuals are able to learn when participating in an appropriately organized system when they could not do so in isolation. The present paper takes as its starting-point the general principles of March's conceptualization of a collective learning system and links them with work from the domains of human resources and strategic management, to ask the question: How do certain organizational policies effect organizations conceptualized as mutual learning systems? Since the learning system March described in 1991 is in essence evolutionary, the organizational policies selected for investigation here were those likely to impact learning through their role in variation (exploration), and in selection and retention (exploitation). I define learning here, rather simply, as ‘the acquisition of useful knowledge’ and vicarious learning as the acquisition of useful knowledge from others rather than through direct experience. Organizations provide a context in which vicarious learning is facilitated and encouraged. Indeed, it has been suggested that it is their knowledge-sharing properties that accounts for their existence (e.g., Conner & Prahalad, 1996; Grant (1996a) and Grant (1996b)). One way in which organizations disseminate knowledge among their members is through routines and standard operating procedures (Cyert & March, 1963; Levitt & March, 1988; March & Simon, 1958). As Levitt and March (1988, p. 320) note: “The experiential lessons of history are captured by routines in a way that makes the lessons, but not the history, available to organizational members who have not themselves experienced the history.” There is a relatively long tradition of considering organizations as learning systems, and as repositories and conduits of knowledge. While Barnard (1938) notes the organization's utility in achieving ends that require cooperation, he also suggests, as Galbraith (1974) and Egeloff (1982) did later, that organizational structure arises from the need to pass information efficiently. Operations management has a rich literature dealing with learning in organizations (e.g., Argote, 1999; Argote & Darr, 2000; Argote et al., 2000; Argote, McEvily, & Reagans, 2003; Epple, Argote, & Devadas, 1991). The role of routines as a means of holding and disseminating knowledge throughout the organization has been examined by March and Simon (1958). Many studies have followed in this vein, dealing specifically with organizational routines (e.g., Cyert & March, 1963; Levinthal & March (1981) and Levinthal & March (1993); Levitt & March, 1988; Lounamaa & March, 1987; March, 1988; March, Schulz, & Zhao, 2000; March & Simon, 1958; Miner, 1994; Nelson & Winter, 1973; Nelson, 1987; Winter, 1987). During the 1990s, knowledge and its role in the firm was the focus of much activity (Galunic & Rodan, 1998; Grant (1996a) and Grant (1996b); Kogut & Zander (1992) and Kogut & Zander (1993); Nonaka & Takeuchi, 1995), and there was renewed interest in the topic of organizations as learning systems (Bruderer & Singh, 1996; Cohen, 1991; Cohen & Levinthal, 1990; Lant & Mezias, 1992; Levinthal, 1991; Levinthal & March, 1993; March, 1991; March, Sproull, & Tamuz, 1991; Simon, 1991). Individual experiential learning relies on the temporal or spatial proximity of stimuli that are potentially causally related (Bullock, Gellman, & Baillargeon, 1982; Fiske & Taylor, 1991). Experiential learning involves considering the outcomes of many trials over time and selecting the action that yields an outcome closest to a desired goal (March & Simon, 1958). However, in many situations, clear correlations between cause and effect are hard to detect. When environments are complex and much is changing simultaneously, the links between actions and outcomes are often ambiguous (Levinthal, 1991; Levinthal & March, 1993; Levitt & March, 1988; Lounamaa & March, 1987). Yet March (1991) showed that learning is possible, even where considerable causal ambiguity exists, if individuals are part of an organized system. Although March (1991) has been criticized for presenting an overly narrow and stylized view of organizations, the strength of his original conception lies in its general insight about collective learning in ambiguous settings, regardless of the specifics of its implementation. March's model is a useful starting-point for further theorizing about organizational learning because it presents a mechanism whereby collectives can learn in situations where individuals on their own cannot. Building on this model, the present paper speculates about a variety of individual- and organizational-level processes that affect variation (exploration) and selection/retention (exploitation) of beliefs—processes that may therefore have an impact on organizational learning. Two classes of variance-inducing mechanisms are considered. The first centers on the propensity of individuals to experiment and the influence of two different forms of restraint on experimenting, one organizational and the other individual. The second is turnover in organizational membership. Alternative selection mechanisms considered here include the use of tenure rather than performance as the criterion for promotion to the organization's policy-making elite, the stringency of the entry requirements to that group, and the extent to which a person's cumulative performance or ‘track record’ rather than their most recent performance is used as the yardstick for promotion decisions. The paper is organized as follows: Section 2 presents the theory and sets out some propositions. Section 3 then explains how the simulation was implemented. After a presentation of the results, there follows a discussion and some conclusions.
نتیجه گیری انگلیسی
[Nokia] is a meritocracy, a place where you are allowed to have a bit of fun, to think unlike the norm, where you are allowed to make a mistake. (Jorma Ollila, quoted in Fox, 2000). Nokia's Chief Executive draws attention to two features of importance for learning organizations: meritocracy, necessary as a selection mechanism to ensure that organizational knowledge is exploited, and experimentation or exploration, necessary to ensure that an adequate source of variation is present. The simulation described here suggests that systems of mutual learning require not only the means by which variation can be generated, such as individual experimentation or turnover in membership, but also a relatively strict application of performance-based selection processes that take account of shorter performance histories, particularly in a dynamic setting. Foolishness emerges as the most effective mode of experimentation and the types of organizational constraint on experimentation modeled here were not useful at all. In practical terms, one might ask individuals or teams to ‘rediscover’ in a systematic way knowledge that they and the organization believe that they already have. This would overcome the drawback of the constrained modes of experimentation that preclude testing the environment in domains that are thought to be well understood. Ensuring that promotion is tied to performance is crucial to organizational learning. While this may seem self-evident, prior research suggests that merit-based promotion is by no means ubiquitous. If we are moving toward a knowledge-based economy and if knowledge, as is frequently claimed, is becoming more important to firm performance and survival, then seniority-based promotion will pose a problem, particularly for firms in high velocity environments. Similarly, increasing the degree to which individual performance history is factored into promotion decisions also has a negative effect on learning, again particularly in a dynamic environments. This presents managers with a problem that the model does not address: how to achieve a level of confidence in making promotion decisions on apparently insufficient information. It could also lead to the emergence of a very fluid organization, one in which people are promoted, but also demoted, frequently, an environment that many might find troubling. The organizational context at Microsoft described by Thielen is one in which the level of experimentation and the rate of turnover for poor performance are both high, and promotions are based largely on people's most recent performance. Microsoft's policies could be construed as being well suited to promote organizational learning, even though this creates what Theilen (1999, p. 72) describes as a “Darwinian environment”. One avenue for future work would be to simulate the same kinds of phenomena as have been investigated here using different simulation ‘substrates’, i.e., using other collective learning systems to which these same set of tests might be applied. An obvious candidate for such an alternative would be a simulation model that does away with the policy-making elite. The present model is in the tradition of March's earlier work with Cyert and is consistent with the work on organizational routines in the behavioral economics literature. A model in which individuals learn not through conforming to a set of rules and routines but by learning directly from one another would have much in common with learning models from the literature on social networks. In such a model, individuals would have chance encounters with other members of the organization and choose whether or not to update their beliefs after an exchange of information with the colleague they have just met. Yet another avenue for future work would be to test these propositions empirically. The aim here has been to build on March (1991) as a means of theorizing about the implication for organizational learning of a number of real-world organizational practices. However, much remains to be done to complete a link between the theoretical conceptualization and learning processes in real organizations. Whether researchers explore these processes using alternative simulations or whether they move into the field to test some of the simulation's predictions, March's model of mutual learning remains an important conceptual cornerstone in the understanding of organizations as learning systems.