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Over the last 15 years, research in Strategy has increasingly recognized that (static) strategic planning is insufficient to respond to complex and fast moving environment; strategy must emerge in response to the moving environment.

Of course, this insight mirrors the evolutionary view at the industry level:

‘I cannot take the other players as static in my battle plan, and to think through all possible scenarios is too difficult.’

Handbook of New Product Development Management

For example, Burgelman and Rosenbloom (1989) observe that technol-ogy strategy is not fully decided but also partially follows the trajectories of industry and internal past decisions capabilities, partially dictated by the environment, and partially ‘selected out’ by the environment, and thus, an evolutionary perspective is instructive. Nelson and Winter (1982: 257) had already described this as technology trajectories. Hamel and Prahalad (1994) emphasize that technology decisions today refer to markets in the future, the competitive rules and customer preferences of which not only are not fully known, but also actually do not yet exist and are shaped by the very actions the firms take. Teece et al. (1997) viewed identifying new opportunities and organizing oneself to embrace them as more important than strategic planning of a competitive positioning – in other words, emerging strategy is required.

Pisano (1997) showed in the pharmaceutical industry that learning about technologies (through process development and feeding back the learning to product development) is an important basis for developing flexible strategies.

Strategy literature has taken two approaches to the problem of lacking fore-seeability and emergence. First, Kester (1984) introduced the concept of ‘real options,’ or the creation of opportunities (but not obligations) of taking some course of actions. The term coined in parallel to financial options, has been adopted in strategy, technology strategy in particular, because technological knowledge represents assets with exactly this feature of an opportunity of taking some course of action. The idea is that firms no longer can commit to investments associated with single scenarios but must maintain the flexibility of pursuing alternatives (Bowman and Hurry, 1993; Kogut and Kulatilaka, 1994; Williamson, 1999). A portfolio of ‘experiments’ represents options that are not optimal in a static sense but, rather, offer robustness: they can become relevant under different market or technology scenarios (Beinhocker, 1999).

The more ambiguous and less describable the environment becomes, though, the less explicitly and quantitatively real options can be used in decision making (Adner and Levinthal, 2004).

A second response to emergence in strategy literature is that of learning and capability building.¹ As a prominent example, Leonard-Barton (1992) observed that every innovation project represents an opportunity for the orga-nization to learn something that can be added to its core capabilities. An organization’s capability are embedded in its technical systems (such as pro-cedures and processes), managerial systems (such as incentives and promotion criteria), skills and knowledge (tacit as well as explicit knowledge in manuals),

1 Of course, learning and capabilities could be seen as ‘options’ as well. However, they are different in a fundamental way because a capability allows a broad set of ‘actions’ that are not specific to any particular scenario; because of this generality and ‘fuzziness’ they cannot be quantified in their value as real options can (indeed, quantitative treatment is at the heart of options theory).

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and values (such as aesthetic judgments of what a good opportunity is). These capabilities are not fully conscious or articulated and not easy to change.

Therefore, core capabilities can become ‘core rigidities,’ or barriers of change and innovation (Leonard-Barton, 1992). They subtly and possibly unconsciously limit the breadth of problem solving (e.g., because things are taken for granted), prevent an organization from adopting certain novel tools, tempt an organization to screen out certain external knowledge (e.g., because it seems inappropriate or is immediately judged irrelevant, remember the classic

‘not invented here’ syndrome), and limit experimentation. Indeed, any tech-nological change happens in the context of a sociological system of technical externalities (such as the need for associated change in complementary prod-ucts, see Cusumano et al., 1992) and social externalities (such as the need for related actors to change their beliefs or attitudes, see Tushman and Rosenkopf, 1992), which often makes the environment of the innovation conservative and resisting.

A particular prominent example of core rigidities is Christensen’s ‘Innova-tor’s Dilemma’ (Christensen, 1992a,b, 1997). Large companies are driven by large opportunities and, in eternal need to focus, are pushed by their sense of relevance and incentives, or by their locked-in investment in a certain product architecture (Henderson and Clark, 1990), to overlook or dismiss niche oppor-tunities served by new technologies that, in the short run, are not competitive in the main market. However, technical progress in the niche may be faster than in the main market, and the time may come when the niche technology (while possibly still inferior to the old technology on the traditional perfor-mance measure) is ‘good enough’ for the main market and even superior on some new dimension, and so the niche technology takes over the main market. Moreover, the incumbent is caught unaware by the small startup that had enough incentive to pursue the niche. This is by no means inevitable, but the danger exists.

Leonard-Barton (1995) develops a set of practices that can help large orga-nizations to recognize and act upon technological opportunities: distributed experimentation and creativity throughout the organization, following shared criteria of funding and evaluation and disseminated results (‘shared problem solving’), ongoing efforts to keep introducing new tools and processes, ongo-ing experimentation and prototypongo-ing, and openness of the organization to information and learning from the outside, both from customers and partners.

Eric von Hippel (von Hippel, 2001; Thomke and von Hippel, 2002) popular-ized the idea that in many cases, the source of innovative ideas lies outside the organization, often with users. They have deep knowledge about the use environment, and they have a stake in the development of the innovation because they can reap the benefits of it.

In the face of high uncertainty and the emergence of unforeseeable events and circumstances, adaptation rather than planning is fundamental. Two

Handbook of New Product Development Management

fundamental ways exist of accomplishing adaptation (Pich et al., 2002): exper-imentation, or trial-and-error learning (Leonard-Barton, 1995 refers to it as

‘product morphing’), and ‘selectionism,’ or parallel trials, choosing the best-performing ex post (Leonard Barton calls this ‘vicarious selection’). Both approaches are widely used and have been observed in literature (for example, Chew et al., 1991, or McGrath, 2001). Theory and empirical evidence suggest that trial-and-error learning offers a higher potential when uncertainty is high, while selectionism offers higher potential when the complexity of the situation (many interacting influence factors and variables) is high (Sommer and Loch, 2004, Sommer and Loch, 2006, Loch et al., 2006). An important application of selectionism is given in Christensen and Raynor (2003). They offer exam-ples how companies can develop emerging capabilities and strategic positions by flexibly experimenting with small entrepreneurial organizational units that are dynamically created and eliminated.

A different approach in the emergence of strategy is the proposal to attempt deliberately to broaden innovation based on new dimensions of competition.

A concept that has been influential in practice is value innovation (Kim and Mauborgne, 1997, 2005), a structured method to discover hidden and under emphasized performance parameters for a product or a service. Once such additional dimensions of product performance have been identified, one might be able redefine the rules of the competitive game by reducing the performance offer on obsolete parameters and investing ahead of the competition in the yet undiscovered performance parameters. This is, on the one hand, a creativity technique to derive new product feature dimensions, but it is also a strategic orientation of seeking new territory rather than competing for established territory. Of course, developing products with new dimensions is risky, requires experimentation (as discussed above), and may fail. Value innovation is related to Hamel’s (2000) idea of Business Concept Innovation, where he makes a related point of widening the set of dimensions.