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   Source : Handbook of Strategy and Management   

Strategic Change Issues Associated with Life Cycle Dynamics

The genesis of life cycle dynamics in the strategy literature may be traced to early work in technology studies. Two counter-forces shape the development and diffusion of technological systems. One is a ‘law of progress’ (Adams, 1931) that points to an exponential growth in the development of a technological system after a relatively slower start. A second force is the ‘law of limits’ that represents the physical limits one invariably confronts with the performance of a technological system. Together, these two forces combine to prescribe an ‘S’ shaped curve in the development and diffusion of a technological system (Foster, 1986).

Life cycle dynamics implicit in the ‘S’ shaped curve were productively employed in other disciplines as well. In the marketing literature, for instance, these dynamics are manifest in product life cycle issues (Kotler, 1994; Mahajan et al, 1990). In the economics literature, life cycle dynamics are apparent in the works of economists such as Vernon (1966). In the organizational field, life cycle dynamics can be found in conceptualizations of organizations progressing from one crisis to another as it grew in scale and scope (Greiner, 1972). They are also implicit in the contagion models that have been employed in diffusion studies and the creation of bandwagons in the development of fads and fashions (Abrahamson, 1991; Rogers, 1983). Clearly this is not an inclusive but an indicative list of those who have contributed to this way of thinking. However, as is apparent from even this short survey, life cycles unfold at various levels.

Several issues confront practitioners associated with processes exhibiting life cycle dynamics. First, there is a need to determine the stage in the life cycle of the organizational entity that is undergoing change. Monitoring internal and external contexts is an approach that has been advocated for this purpose. Although monitoring might appear to be a routine task, cognitive biases may create many difficulties in accomplishing this task (Kahneman et al., 1982; Kiesler and Sproull, 1982; Dutton and Jackson, 1987). Despite these difficulties, some tell-tale signs that have been employed to determine what stage an industry might be in its development are product price, the level of commoditization, the number of new entrants and exits.

In addition to correctly recognizing the stage of development of the entity being examined, another managerial challenge is determining the appropriate mode of operation in each stage of a life cycle. For instance, Utterback (1994) suggests that strategy implies competition based on functionalities during a ‘fluid’ stage of technology development whereas it implies competition based on reliability, quality and price during a ‘specific’ stage of development. Similar considerations have led others to suggest that a firm should be organized to ‘explore’ during early growth stages and organized to ‘exploit’ during later stages (March, 1991).

The most difficult challenge in managing processes driven by life cycle dynamics is to make transitions in between stages. Transitions are difficult as they imply changing one set of competencies well suited for one stage of operation to a different set of competencies required for a different stage of operation. Indeed, appropriate forms of behavior at one stage of operation may be the very forces that prevent organizations from transiting to the next stage. In other words, transitions become difficult as competencies at one point become traps (Levitt and March, 1988; Leonard-Barton, 1992).

While life cycle models are seductively simple to understand, they are easy for managers to misread. For instance, in the development of cochlear implants (a bio-medical prosthetic device), proponents of the single-channel device that gained early FDA approvals concluded to their peril that industry dynamics had switched to a growth and maturity stage (Garud and Van de Ven, 1992). This belief turned out to be misplaced when other firms continued developing their cochlear implants under the assumption that the industry was still at an introductory stage.

In a similar vein, Henderson (1997) illustrates how beliefs about the limits of a technology based on its internal structure can be misleading. Using the development of optical photolithography as an example, Henderson shows how the ‘natural’ or ‘physical’ limits of the technology were relaxed by unanticipated progress on three fronts: significant changes in the needs and capabilities of users, advances in the performance of component technologies (lenses), and unexpected development in the performance of complementary technologies. These observations lead Henderson to caution against using a life cycle model to predict the limits of a technology. Such predictions must be tempered by a recognition that many other factors (beyond the immediate grasp of those forecasting) may play a role in extending the life of a technology.

Life cycle dynamics are at play in a key field that drives change in contemporary times -semiconductors. For about three decades, Moore's law described progress that has been made with semiconductor chips - a doubling of the number of chips that might fit into a silicon chip every 18 months. Announcements by scientists at Intel suggest that the silicon substrate may be reaching its limit (Markoff, 1999). In Grove's terminology, these limits may represent the onset of a strategic inflexion point with the potential to create a ‘10X change’ (Grove, 1996). As this limit is reached, semiconductor firms will have to decide whether to continue with silicon chips, shift to a new architecture or to a new substrate. To ensure that Intel makes appropriate decision as it encounters this and other such inflexion points, Grove and his colleagues have put in place ‘dialectical processes’ that shape decision making at Intel. We explore issues associated with dialectical processes as they pertain to strategic change in the next sub-section.

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