Activity

Computational inquiry into human nature originated in the years after World War II. Scientists mobilized into wartime research had developed a series of technologies that lent themselves to anthropomorphic description, and once the war ended these technologies inspired novel forms of psychological theorizing. A servomechanism, for example, could aim a gun by continually sensing the target's location and pushing the gun in the direction needed to intercept it. Technologically sophisticated psychologists such as George Miller observed that this feedback cycle could be described in human-like terms as pursuing a purpose based on awareness of its environment and anticipation of the future. New methods of signal detection could likewise be described as making perceptual discriminations, and the analytical tools of information theory soon provided mathematical ways to talk about communication. In the decades after the war, these technical ideas provided the intellectual license for a counterrevolution against behaviorism and a restoration of scientific status to human mental life. The explanatory power of these ideas lay in a suggestive confluence of metaphor, mathematics, and machinery. Metaphorical attributions of purpose were associated with the mathematics of servocontrol and realized in servomechanisms; metaphorical attributions of discrimination were associated with the mathematics of signal and noise and realized in communications equipment; and metaphorical attributions of communication were associated with the mathematics of information theory and realized in coding devices. The new psychology sought to describe human beings using vocabulary that could be metaphorically associated with technologically realizable mathematics.

The development of the stored-program digital computer put this project into high gear.

Introduction

What makes firms successful over long periods of time? Many organizational theorists have argued that organizational environments evolve through long phases of incremental change, punctuated by radical transformations. Because changing environments can dramatically alter the basis of competitive success, organizations need to transform their mode of operation to enhance their prospects of future success. Change by itself, however, is not sufficient to deal with shifting criteria of market success: organizations have to change in a speedy manner in order to avoid losing their business to incumbent rivals or new entrants who are able to offer better, cheaper, or radically new products. If organizational decision makers cannot react with sufficient speed to new competitive challenges, attempts to align the organization with a changing environment may come too late and be entirely fruitless.

A large amount of empirical evidence has demonstrated the difficulty of firms to correctly foresee technological developments or even discern these shifts while they are occurring (Maclaurin, 1949; Tushman & Anderson, 1986; Henderson & Clark, 1990). As a result, the speed in which organizations adapt to major environmental changes can represent a crucial factor in reducing or amplifying the negative impact of a lack in technological foresight. The same is true for the case of technological oversights: the faster an Organization responsiveness to environmental shock 261 organization can correct its failure to ascertain shifts in the environment, the better are its chances to limit the damage such oversights can have on the future success of its business.

Introduction

That organizations, broadly defined, are cognitive structures is an observation at once trivial and profoundly complex. It is trivial because, at some level, it is a commonplace that organizations can (or at least should) learn, in something quite analogous to the sense in which people learn. That organizations “process information” is a staple of organizational-behavior theory. When examined in detail, however, the identification of organizations with cognitive structures raises more questions than it answers. In exactly what sense does an organization learn or perceive? This chapter will not attempt to resolve all these complexities; ultimately, it may not even stray far from the trivial. But it will attempt to frame the issue of organizational cognition in a general way and to apply that idea to the problem of “organizational perception” that is, to the problem of why organizations seize, or fail to seize, profitable opportunities.

The chapter proceeds as follows. Relying on some perhaps idiosyncratic sources in cybernetics, the theory of information, and cognitive theory, Section 2 sets forth a general – indeed, rather abstract – picture of knowledge, information, and learning. Section 3 applies that picture to the issue of organizational perception: Why are some organizations, again broadly defined, able to notice and seize opportunities for profitable innovation while other organizations are not? Drawing on the evolutionary theory of economic capabilities, that section goes on to work up a typology of the causes of innovative success and failure. Section 4 canvasses the history of the computer industry for examples to fit the typology.

When rapid environmental change is endemic, old methods fail

Many accounts of innovation, entrepreneurship, change, and growth have focused on the successful efforts of “high tech” firms pioneering changes the rest of industry eventually had to adapt to. The “low tech” or “mature” firms seeking to adapt have often been characterized as change-resistant organizations whose sluggish, inept managerial modes fail to accomplish effective response. Fair or not, the popular impression is that high tech firms are innovative, entrepreneurial, and flexible, whereas mature firms are not.

Successful major innovations in mature-industry firms are all the more impressive because they must overcome a variety of innovation dilemma that were identified long ago (e.g., Quinn 1979; Miller & Friesen 1980). Slow response to external change can be followed by frantic, ineffectual efforts to catch up. Scarce R&D dollars may be devoted to poorly understood projects, particularly where little research or product development has taken place for years, and radical change can derail firms already near the edge. New developments may be starved in vain hopes of preserving once-dominant products, on the reasoning that successful products should not be cannibalized. Or new efforts may simply fail.

In short, for mature businesses especially, innovation is often risky, poorly understood, and poorly executed. All these responses disclose a common failure to respond entrepreneurially to the changing competitive environment. Examples will illustrate their relevance today.

Introduction

For both individuals and organizations, most behavior is habitual. In a given situation, people do what they have usually done. When their behavior changes, it is often gradual, as behavior patterns are shaped by the feedback that they evoke. Producing good outcomes increases a behaviors' chance of being performed again, whereas unpleasant outcomes increase the search for alternative behaviors. Learning may be defined as appropriate change or consistency (Levitt & March, 1988).

The learning process involves a series of decisions, where one option is business as usual and the other options constitute changes – either innovations or reversion to earlier behavior. Whenever learning is possible, each option must be evaluated in terms of the outcomes it can cause and the lessons it can teach. For example, a short-term loss might be weighed against the chance to learn something generally useful (e.g., passing up a favorite watering hole in order to try a new restaurant, pulling one's best sales rep off solid accounts in order to test a new territory) (Einhorn, 1986).

Decision theory provides a set of well-articulated analytical methods for making such choices (Kleindorfer, Hershey, & Kunreuther, 1992; Raiffa, 1968; Watson & Buede, 1986; von Winterfeldt & Edwards, 1986). It can incorporate both an action's direct impacts and its informational value. The value of information can be both instrumental (through improving future decisions) and intrinsic (through satisfying curiosity, reducing the aversive-ness of uncertainty, or providing the “thrill value” of trying something different).

Introduction

In keeping with the theme of this book on technological oversights and foresights, this chapter examines the forms of intelligence that can guide the process of developing and commercializing technological innovations. The innovation process is an ambiguous and uncertain journey in which entrepreneurs, with financial support and approval from top managers or investors, undertake a sequence of events to develop and transform a vague novel idea into a concrete implemented reality. Several years of intensive investment and effort are often required to develop an innovation to the point where its end results can be determined. As a consequence, much of the innovation journey involves an adaptive learning process to deal with conditions of ambiguity (i.e., where it is not clear what specific preferences or objectives should be pursued to reach a vague superordinate goal) and uncertainty (i.e., where it is not clear what means of actions will achieve desired outcome goals).

However, two prior field studies of learning processes during innovation development by Van de Ven and Polley (1992) and Garud and Van de Ven (1992) found no evidence of trial-and-error learning during the initial pre-market period of innovation development, but clear evidence of it during the ending market-entry period when the specific innovations were being commercialized and introduced in the market. Trial-and-error learning is an adaptive process in which entrepreneurs continue with their course of action if the outcomes associated with it are positive, and they change their course of action if the associated outcomes are negative.

This book is about technological innovations. It is set against a background of contemporary technological turbulence and numerous attempts to describe the genesis of various changes in technology that have been observed. It represents an attempt to make sense of the technological dynamism that appears to be endemic to the times and to provide some plausible suggestions for managing it.

The task is not an easy one. In particular, it is a task that is complicated by the conspicuous importance of technological innovation to modern life. There is no shortage of stories of innovation. They fill biographies, the popular press, and the after-hours conversations of participants. These stories have certain similarities. Changes in technologies, practices, and products are characteristically described in terms of the triumph of the new over the old. Initially, a new idea, institution, or practice is introduced into a small part of the system. Ultimately, it becomes pervasive. The stories use the powers of retrospection to identify individual and organizational genius in this triumph of good over evil.

The obvious difficulty with these stories is not so much that they can be demonstrated unambiguously to be false as it is that even if they were false we would nevertheless construct and repeat them. The market for stories of individual and collective triumph is insatiable, and anthropomorphic biases in human storytelling and comprehension are legendary. As a result, the fact that the tales of innovation that are told seem to have a consistent structure is hardly compelling evidence for their validity.

Introduction

Accurate technological forecasting is extraordinarily important. It can allow managers to shape technology strategy, to prevent continued investment in technologies that are long past their prime, and to guard against premature commitment to untried technologies whose long-term potential is limited. Unfortunately, accurate technological forecasting is notoriously difficult (see some of the other chapters in this volume).

In this chapter, I hope to contribute to the practice of technological forecasting through a detailed analysis of the usefulness of one tool that has been widely advanced as a guide to technological foresight: the technology life cycle (Van Wyk, 1985). I use the history of optical photolithographic alignment technology to suggest that the uncritical application of the life cycle as a forecasting tool may have quite dangerous implications. The life cycle is a useful ex post descriptive device: appropriate proscriptive use of it requires a detailed understanding of the underlying technological, economic, and social dynamics on which it rests, in combination with a critical awareness of the ways in which industry acceptance of the life cycle as a descriptive tool can obscure these dynamics, making it very difficult to use it with any accuracy.

Industry pundits have been confidently using the technological life cycle to predict the death of optical photolithography since 1977, yet it remains the tool of choice in leading-edge semiconductor production (Figure 9.1). The factors that lie behind this singularly unsuccessful forecasting effort throw considerable light on the limits of the technological life cycle as a forecasting tool.

Introduction

Schumpeter's evolutionary theory of innovation suggests that the “gales of creative destruction” brought about by radical innovation will allow some organizations to gain competitive advantage and result in “… old concerns and established industries … perish[ing] that nevertheless would be able to live on vigorously and usefully if they could weather a particular storm [of this creative destruction]” (Schumpeter, 1942, 90). Although Schumpeter's argument does not specifically identify the organizational locus of the innovation, it does note that “new concerns or industries that introduce new commodities or processes” are likely to displace older industries (Schumpeter, 89).

Radical innovation and its organizational foundations became an important area of study in subsequent academic inquiry on innovation. Many commentators have highlighted the advantage small and de novo firms have over incumbent firms in generating and incubating radical innovations (Foster, 1986; Jewkes et al, 1958; Tilton, 1971; Abernathy & Utterback, 1978; Mitchell, 1989; Henderson & Clark, 1990). This suggests two key research questions.

Introduction: Bureaucracy and innovation

Social scientists have long attempted to understand the social and organizational forces that enhance or undermine innovation in corporate settings. The products of such investigations, their proponents claim, may be implemented by managers in order to enhance corporate performance (Kanter, 1988). In particular, many programmatic texts on the nature of R&D and other organizations have claimed that “bureaucracy” undermines the conditions necessary for individual and organizational innovativeness (Ritti, 1971; Rothman & Perucci, 1970; Shenhav, 1988). In this view, widely propagated by both academics and popular writers, and supported by broadly held commonsensical beliefs, rigid hierarchical structures bolstered by rules and regulations, formalized procedures, and “red tape,” constrain the social environment of professional work and suppress the intrinsic motivation (Deci, 1975; Shapira, 1989) and innate capacity for creativity (Amabile, 1988) that individuals are capable of bringing to it. Bureaucracy, the argument goes, encourages conformity and particularism and discourages playfulness, exploration, and risk taking. Such environments produce cautious, conservative, and uninspired “organization men” for whom “playing it safe” and “cover your ass” are the primary rules of organizational survival, whereas innovation and its consequences pose a continuing threat (see Perrow, 1986). These pathologies of bureaucracy are particularly detrimental to the performance of R&D organizations in which technological foresight and oversight are crucial factors (see Garud, Nayyar, & Shapira, this volume, chapter 1): bureaucratic culture enhances the probability that technological foresights are suppressed and facilitates those processes that encourage oversight and allow it to become the norm; anything else, in this view, threatens the stability of the existing order and the particular interests that support it.

Introduction

An everyday observation about technological innovation is that some firms seem to make better technological choices than others. Common sense tells us that innovators have great technological reach, that they possess an uncanny capacity for divining the marketplace and offering timely and exciting products or services. It is difficult not to attribute these differential rates of success to the technological “foresight” of first movers and/or to the technological “oversights” of laggards. First movers are visionaries with a knack for making the right choice among highly uncertain technological options. Laggards, on the other hand, miss opportunities by being wedded to the past, and thus forego future gains. These dispositional attributions frame foresight and oversight as issues of cognitive competence – some firms have technological vision, while others don't. Moreover, to the extent that cognitive competence can be measured and manipulated, dispositionalizing means that the propensity for foresights and oversights can be engineered. Technologists, consultants, and managers can create organizing routines that promote creativity, enhance the innovativeness of design, and generally incite firms to be recipe-makers rather than recipe-takers.