1. Selecting Product Architectures and Business Models 

The design and performance specification of products, and the business model employed, all help define the manner by which the enterprise delivers value to customers, entices customers to pay for value, and converts those payments to profit. They reflect management’s hypothesis about what customers  want and how an enterprise can best meet those needs, and get paid for doing so. They embrace: (1) which technologies and features are to be embedded in the product and service; (2) how the revenue and cost structure of a business is to be “designed” and if necessary “redesigned” to meet customer needs; (3) the way in which tech- nologies are to be assembled; (4) the identity of market segments to be targeted; and (5) the mechanisms and manner by which value is to be captured. The function of a business model is to “articu- late” the value proposition, select the appropriate technologies and features, identify targeted market segments, define the structure of the value chain, and estimate the cost structure and profit potential (Chesbrough and Rosenbloom, 2002: 533–4). In short, a business model is a plan for the organizational and financial “architecture” of a business. This model makes assumptions about the behavior of revenues and costs, and likely customer and competitor behavior. It outlines the contours of the solution required to earn a profit, if a profit is available to be earned. Once adopted it defines the way the enterprise “goes to market”. Success requires that business models be astutely crafted. Otherwise, technological innovation will not result in commercial success for the innovating enterprise. Gener- ally there is a plethora of business models that can be designed and employed, but some will be better adapted to the ecosystem than others. Selecting, adjusting, and/or improving the business model is a complex art.

Nevertheless, the importance of “business models” has been given short shrift in the academic literature, at least until quite recently. Important (business model) choices include technological choices, market segments to be targeted, financial terms (e.g. sale versus leasing), choices with respect to bundled versus unbun- dled sales strategies, joint ventures versus licensing versus go-it- alone approaches, etc. For example, in the early days of the copier industry, Xerox focused on leasing rather than selling copiers. This stemmed from a belief that customer trial would lead to further use. Another example from the USA is Southwest Airlines, which believes that most customers want low frills, reliability, and low cost. It eschews the hub-and-spoke model, does not belong to any alliances, and does not allow interlining of passengers and baggage. Nor does it sell tickets through travel agencies—all sales are direct. All aircraft are Boeing 737s. Its business model is quite distinct from the major carriers, although many have tried (without much success) to copy elements of the Southwest model.15

The capacity an enterprise has to create, adjust, hone, and, if nec- essary, replace business models is foundational to dynamic capa- bilities. Choices around how to capture value all help determine the architecture or design of a business. Having a differentiated (and hard-to-imitate) yet effective and efficient “strategic architec- ture” to an enterprise’s business model is important. Both Dell Inc. and Wal-Mart have demonstrated the value associated with their business models (Webvan and many other dot-coms demonstrated just the opposite). Both Dell Inc.’s and Wal-Mart’s business models were different, superior, and hard for competitors to replicate. They have also constantly adjusted and improved their processes over time.16

One might be tempted to argue that designing, implementing, and validating business models is straightforward, but this sim- ply is not so. Aspects of designing (and redesigning) a business model are undoubtedly readily routinized and codified. Note the plethora of business books providing instruction on how to write a business plan. Such manuals can provide some discipline to the business model questions that one should ask. However, designing a new business requires creativity, insight, and a good deal of customer, competitor, and supplier information and intelligence. There is a significant tacit component. Entrepreneurs and execu- tives are forced to make many informed guesses about customer and competitor behavior, as well as the behavior of costs. Indeed, validating a business model and a business plan requires both effort and judgment. It takes detailed fact-specific inquiry including: a keen understanding of customer needs and customer willingness to pay; an understanding of procurement cycles and the sales cycle; knowledge of supply and distribution costs; and an understanding of competitor positioning and likely competitive responses. Put differently, selecting the right “architecture” for a business requires not just understanding the choices available; it also requires assem- bling the evidence needed to validate conjectures and hunches about costs, customers, competitors, complementors, distributors, and suppliers.

Designing good business models is in part “art”. However, the chances of success are greater if enterprises (1) analyze multi- ple alternatives, (2) have a deep understanding of user needs, (3) analyze the value chain thoroughly so as to understand just how to deliver what the customer wants in a cost-effective and timely fashion, and (4) adopt a neutrality or relative efficiency perspective to outsourcing decisions. Useful tools include mar- ket research and transaction cost Chesbrough and Rosenbloom (2002) suggest that established enterprises often have blinders with respect to alternative business models—and that this prevails even if the technology is spun off into a separate organi- zation, where other (path-dependent constraints) are less likely to exist.

In short, designing the business correctly, and figuring  out what John Seeley Brown refers to as the “architecture of the revenues”17 (and costs), involve processes critical to the forma- tion and success of  new  and  existing  businesses.  No  amount of good governance and leadership is likely to lead to success if the wrong business model is in place. Good business models achieve advantageous cost structures and generate value propo- sitions acceptable to customers. They will enable innovators to capture a large enough portion of the (social) value generated by innovation18 to permit the enterprise at least to earn its cost of capital.

2. Selecting Enterprise Boundaries 

In regimes of rapid technological progress, setting the enterprise boundaries correctly is important, and can be viewed as an ele- ment of getting the business model right. In Teece (1986a), Ches- brough and Teece (1996), and Teece (2000), normative rules were advanced indicating how enterprise boundaries ought to be set to ensure that innovation is more likely to benefit the sponsor of the innovation rather than imitators and emulators. Key elements of this framework were: (1) the appropriability regime (i.e. the amount of natural and legal protection afforded the innovation by the circumstances prevailing in the market); (2) the nature of the complementary assets (cospecialized or otherwise) that an innovat- ing enterprise possessed; (3) the relative positioning of innovator and potential imitators with respect to complementary assets; and (4) the phase of industry development (pre or post the emergence of a dominate design). The framework is prescriptive not only as to strategy but also as to

Enterprise boundary decisions need to reflect other criteria too. A company’s integration upstream, downstream, as well as exter- nally, is partly driven by the need to build capabilities, particularly when such capabilities are not widely distributed in the industry. Of course, vertical specialization is not itself independent of enterprise strategy, and vice versa (Macher and Mowery, 2004). Studies of the early vertical evolution of the petroleum industry stressed the need to align upstream and downstream capacities in an envir- onment where qualified business partners were limited (Teece, 1976). Pisano, Shan, and Teece (1988: 202) developed a framework for understanding R&D outsourcing that recognized that the locus of world-class research/productive capability might lie external to the enterprise, requiring outsourcing as a way to compete.19 Jacobides and Winter (2005: 398) have also clearly stated that “it is necessary to look at the distribution of productive capabilities—to understand when enterprises are integrated and when they are not. It becomes clear that vertical specialization must be in part a function of heterogeneity in productive capabilities along the value chain.” They also note that the capability development process itself changes as a consequence of changing scope. Recognition that systemic innovation favors integration, for both transaction costs and capability reasons, is also embedded in the saga of the development of the diesel electric locomotive (Teece, 1988). The ability of enterprises to procure technology externally as well as develop it internally are critical skills, as discussed above. Firms must dispel prejudices against technology from the outside, and hone their absorptive capacity through learning activities and skill accumulation. Enterprises may require alliance arrangements to actively learn and upgrade relevant skills (Branzei and Vertinsky, 2006).

The critical strategic element associated with capturing value from innovation is the ability of the innovating enterprise to identify and control the “bottleneck assets” or “choke points” in the value chain from invention through to market (Teece, 1986a, 2000). Outsourcing those assets/services that are in competitive supply is, of course, consistent with such a strategy. In short, the boundaries of the enterprise need to be artfully contoured for each major innovation, using decision criteria referenced above. Failure to do so is likely to be associated with the failure to stimulate market development (especially of complementary tech- nologies) and incomplete capture of the profits available from innovation.

3. Managing Complements and “Platforms”

Investment choices in many high-technology industries today are driven by imperatives quite different from the (industrial) contexts that have animated strategy research over the past half-century. Scale and scope economy “mandates”, which to some strategists dictate the scale and scope of the enterprise, have given way to a different set of mandates around developing (or encouraging) complementary investments and capturing cospecialization ben- efits. The reason for this is that in many industries outsourcing has made scale an industry asset, in the sense that economies of scale can be captured by outsourcing to contract manufacturers who, in the face of competition, pass on the benefits of scale. Witness the contract semiconductor fabricators. They enable fab- less semiconductor “designers” to capture most of the benefits of scale without engaging in manufacturing. Likewise, in the cloth- ing industry, small-scale designers of footwear and outerwear can source at competitive rates from large suppliers, thereby capturing the benefit of scale economics previously enjoyed only by large integrated manufacturers. With competition, scale advantages are not proprietary, and are unlikely to be a source of sustainable differentiation.

When intermediate (product) markets are well developed, nei- ther economies of scale nor economies of scope need define the scale and scope of the enterprise. Contractual access (on competi- tive terms) to scale-based “facilities” vitiates the need for enterprise scale and scope. This was the major theme in Teece (1980a) but the importance of the argument was often not appreciated. Today its importance is more evident.

While the importance of scale and scope economies to enterprise boundary decisions may have been softened, the significance to enterprise strategy of cospecialization has been elevated. As viewed by customers, high-technology “products” are often systems. These systems consist of interdependent components resting on “plat- forms”. There is strong functional interdependence amongst com- ponents of the system. End-user demand is for the system, not the platform. There is often a multisided “market” phenomenon at work as well. For instance, electronic game consoles are not much use without games; computer operating systems are not much use without a suite of application programs; credit cards are not much use to cardholders without merchants that will accept them, and vice versa; and hydrogen cars are not much use without hydrogen filling stations, and vice versa. This important class of situations has highlighted the importance of cospecialization, and strategic decision making must now take this into account.

The phenomenon is not new—the automobile industry depended first on the general store and then specialized retail outlets to make gasoline ubiquitously available to motorists. The role of complementary assets and cospecialization has already been recognized in the innovation process, and a decision framework outlined to chart the innovator on a course more likely to lead to a higher share of the available profit (Teece, 1986a, 2000). What is new today is that complements often sit on top of what might be thought of as “platforms”, which are managed by an incumbent enterprise (Evans et al., 2006). In these circumstances, entry decision and “boundary” conundrums exist. The platform owner needs complementary products to be provided by others, particularly when it has little  or no relevant skills to develop them itself. Fostering innovation and entry by the providers of complementary products may, in fact, require the platform man- ager to commit (by word or deed) not to provide certain com- plements. When the interface between the complementors and the platform is itself evolving, decision rules become ever more complex. The platform owner and the complementors might also need to consider whether the platform needs to be open or pro- prietary, and whether tools and other incentives should be pro- vided to stimulate investment by the complementors. Decision frameworks that recognize the importance of network effects, dis- persion in the sources of innovation of complementary products, interoperability issues, and installed base trajectories must all be factored into decisions. Quality decisions will require uncommon foresight and the ability to shape outcomes. In this regard, the existing asset base of the platform manager, including its finan- cial resources, is of considerable significance. The distribution of (development) capabilities between the platform manager and the complementors will also be important. Also, as discussed below, the boundaries of the enterprise (i.e. whether the platform man- ager is also providing complements) is likely to be of signifi- cance, possibly deterring (or encouraging) entry and innovation by complementors.

4. Avoiding Bias, Delusion, Deception, and Hubris

As noted, proclivities toward decision errors are not uncommon in managerial decision making, particularly in large organizations. Investment decision errors already identified include excessive optimism, loss aversion, isolation errors, strategic deception, and program persistence. As Nelson and Winter (2002: 29) note, orga- nizational decision processes often display features that seem to defy basic principles of rationality and sometimes border on the bizarre. These errors can be especially damaging in fast-paced environments with path dependencies and network effects, as there is less opportunity to recover from mistakes. When invest- ments are small and made frequently, there are many opportu- nities to learn from mistakes. Since large investments are usually occasional, major investment decisions are likely to be (potentially) more vulnerable to error.

Fortunately, biases can be recognized ahead of time. Enter- prises can bring discipline to bear to purge bias, delusion, decep- tion, and hubris. However, the development of disciplines to do so is still in its infancy. The implementation of procedures to overcome decision-making biases in enterprise settings is, accord- ingly, not yet a well-distributed skill, and may not be for decades to come. Accordingly, competitive advantage can be gained by early adopters of techniques to overcome decision biases and errors.

Overcoming biases almost always requires a cognitively sophis- ticated and disciplined approach to decision making. Being alert to the incentives of the decision-makers and to possible information asymmetries is a case in point. Obtaining an “outside view” through the review of external data can help eliminate bias. Testing for errors in logic is also essential. Management also needs to create an environment where the individuals involved in making the decision, at both the management and board level, feel free to offer their honest opinions, and look at objective (historical) data in order to escape from closed thinking. Incentives must also be designed to create neutrality when assessing investments in the old and the new.

Considerable progress in combating biases has been made. Advisors call upon managers to adopt radical, nonformulaic strategies in order to overcome the inertias that inhibit break- through innovation (Davidow and Malone, 1992; Handy, 1990). Specifically, corrective strategies encourage change through two basic mechanisms: (1) designing organizational structures, incen- tives, and routines to catalyze and reward creative action; and (2) developing routines to enable the continual shedding of estab- lished assets and routines that no longer yield value. Strategies that provide structures, incentives, and processes to catalyze and reward creative action serve to attenuate problems of excessive risk aversion. For example, strategies that call on the enterprise to “cut overhead” and “increase divisional authority” can be interpreted as efforts to reduce the number of management layers of the enterprise and to push decision making down to lower levels to minimize the inherent isolation errors associated with multilevel, hierarchical decision-making processes. These recommendations can be viewed as organizational processes and strategic mecha- nisms to mitigate decision-making biases.

Perhaps most importantly, executives must acknowledge the interaction effect between owning established assets and decision- making biases. Many recommended strategies (such as cannibal- izing profitable product lines and licensing your most advanced technology) call for the shedding of established capabilities, com- plementary assets, and/or administrative routines to reduce the intensity of decision-making biases. By jettisoning “dead” or dying assets, the enterprise is no longer shackled with an asset base that can be a crutch and provide a false sense of security, and sustain groups inside the enterprise that persist in torpedoing new initia- tives. In abandoning dead or dying assets, the enterprise frees itself of certain routines, constraints, and opportunities for undesirable protective action inside the enterprise.

Sources of the “anti-cannibalization” bias mentioned earlier can also be attacked. Self-serving behavior inside the enterprise to “protect” incumbent constituencies undergirds this bias. Flawed investment frameworks may also contribute. Entry into  a mar- ket by an enterprise with a new and superior technology will cause rapid depreciation of the economic value of an incum- bent’s plant and equipment. However, the incumbent may well make business decisions based on examining accounting profits that reflect depreciation rates specified by accepted accounting standards. If decision-makers confuse depreciation calculated according to general accepted accounting principles (GAAP) with real economic depreciation, and conclude that the existing busi- ness is still profitable when, in fact, it is not, then the business enterprise may eschew profit-enhancing cannibalization of its own products. To guard against this bias, investment decision-makers and incumbents must use accounting data cautiously. In particular, they must also consider the opportunity cost associated with not cannibalizing their own products. Capital-budgeting procedures implicitly biased against projects with long-term horizons must be jettisoned or used cautiously. That is not to say that incum- bents need to invest on the same schedule as new entrants. As Teece (1986a) and Mitchell (1991) demonstrate, incumbents need not be the first movers. Superior positioning in complementary assets may enable incumbents to let the new entrants do the prospecting, investing later once market and technological risk has diminished.

There is an obvious role for leadership in making quality decisions, communicating goals, values, and expectations, while also motivating employees and other constituencies. Organiza- tional identification (and commitment, which is the corollary) can dramatically augment enterprise performance, although it is doubt- ful it can override completely misaligned incentives. Nevertheless, group loyalty is a “powerful altruistic force” that conditions employee goals and the cognitive models they form of their sit- uation (Simon, 1993a: 160). Top management through its action and its communication has a critical role to play in garnering loy- alty and commitment and achieving adherence to innovation and efficiency as important goals. Since there is already an extensive literature on culture, commitment, and leadership, these issues are not discussed further. However, it would be a significant oversight in a summary statement of the dynamic capabilities framework to ignore them completely. Their full integration into the framework is left to others. However, it is recognized that to the extent such properties are not ubiquitously distributed amongst business enter- prises, they can be a very important source of superior perform- ance. Figure 1.2 summarizes the microfoundations identified in this section of the chapter.

Source: Teece David J. (2009), Dynamic Capabilities and Strategic Management: Organizing for Innovation and Growth, Oxford University Press; 1st edition.

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