Time and technological innovation: Implications for public policy

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Technology in Society 28 (2006) 281–301 www.elsevier.com/locate/techsoc

Time and technological innovation: Implications for public policy Robert W. Rycroft International Science and Technology Policy and International Affairs, The Elliott School of International Affairs, The George Washington University, 1957 E Street, NW, Suite 403, Washington, DC 20052, USA

Abstract The speed with which modern technologies are innovated seems to be accelerating and there appears to be some consensus that faster technological change is likely to create substantial problems for public policy makers. But what is the empirical evidence for the impression of more rapid technological advancement? And what does this evidence imply for the future of policy making? Answering these questions involves assessing the empirical research based in four temporal models that form the bases for measuring innovation time (product cycle, barrier–breakthrough, technological discontinuity, and continuous change) according to four analytical approaches (product concentrations, expert opinion, sales growth and decline, and age of cited patents). Based on this assessment, multiple categories of policy-relevant temporal indicators are recommended. r 2006 Elsevier Ltd. All rights reserved. Keywords: Innovation speed; Temporal models; Policy metaphors; Complex policy

1. Introduction Conventional wisdom has it that the pace of technological change is more rapid today than it was in the past. The following assessment of one of the ‘‘major trends’’ in technological innovation, made by a significant European Commission study, is typical of this perception: Acceleration. In general terms, technological innovation has speeded up substantially over the last few decades. This is mainly illustrated by the fact that the time required Tel.: +1 202 994 6615; fax: +1 202 994 1639.

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to launch a new high-tech product has been significantly reduced. The process from knowledge production to commercialization is much shorter today. And product life cycles are shorter too (for low- and medium- as well as for high-tech products). The fast development of ICT [information and communication technologies] has certainly played a key role in bringing about this change [1]. Almost as pervasive as the assumption about innovation speed is the concomitant expectation that rapid technological innovation is bad news for government decision makers. Thus: We see a growing divergence between time cycles of government and those of technology development. Quite simply, this presents government operations with a Hobson’s choice: Either live within a shorter response time and run the risk of illconsidered actions (or inactions) or see government input become less relevant and assume reduced stature [2]. Indications of faster technological innovation certainly seem to be everywhere. Consider the following:

 





Gordon Moore’s assertion in 1965 that the performance of integrated circuits would double every 18–24 months—a prediction so accurate that it has become known as ‘‘Moore’s Law’’ [3]. ‘‘Metcalfe’s Law’’ (named for the original statement by Robert M. Metcalfe, inventor of the Ethernet computer networking protocol) which asserts that the value of a network (e.g., linked computers, telecommunications systems) increases as the square of the number of nodes. And new nodes are often added very swiftly [4]. The time it takes to introduce new technologies appears to have been shrinking dramatically. A 1990 study found that US products took an average of 35.5 months to complete. In 1995, this time was reduced to about 23 months [5]. The introduction of research and development (R&D) intensive products and processes (so-called ‘‘hightechnology’’ products) may occur even faster. According to one analysis, R&D project time in firms fell from 18 months in 1993 to 10 months in 1998 [6]. Often ‘‘flash development’’ teams are used to increase the speed with which the process of moving a technological concept to market is carried out [7].

This is not to say that skeptics don’t exist. One source of caution derives from a sense of de´ja` vu. After all, the technological ‘‘compression of time’’ hampers policy deliberations in ways that are not entirely new. Time has always been a scarce resource in the making of policy, and governments have responded to these pressures with experiments such as forecasting and technology assessment. However, two things may have changed to a degree. The first is that time may be relatively an even scarcer resource than it has been, and that more consequential decisions may have to be made quicker than in the past. The second is that if technology is the temporal problem, it is now often seen as the time-saving solution for government. Information and communications technologies are particularly attractive for policy makers since they hold the promise of being able to reduce, process, and analyze data and information and to implement policy decisions much more rapidly [8].

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Even if the highly visible examples listed above are generally taken at face value, some experts still view them cautiously. For instance, a widely understood paradox of the concept of network effects can be that even though a technology brings a set of new performance characteristics to the market that are very attractive, this does not guarantee consumer acceptance. Often a consumer will only accept the new innovation if it is reasonable to assume that other consumers will make the same decision. Markets with lots of interconnections among consumers—termed ‘‘adoption networks’’—may thus create a dynamic that slows acceptance of the technology, despite the presence of obvious advantages in terms of performance characteristics. So technology may evolve only half as fast, leading to ‘‘Demi Moore’s Law’’ [9,10]. Reliance on new product and process development as the major piece of evidence for reductions in product cycle times also attracts lots of criticism. It is just as reasonable to argue that the critical factor in the speed of technological change is not the innovation of new technologies, but finding new applications for existing technology. For example, an increasingly common innovation pattern is the convergence or fusion of two or more established technologies that result in the generation of a new product (e.g., optoelectronics, micro-motors) [11]. The new fused technology can either invade the markets of the original technologies or coexist with them by filling new market needs. Here the overall pace of innovation is highly unpredictable. Innovation may not be fast (more radical) or slow (more incremental), but some combination of both [12]. This article asks three questions: What are the dominant models that have structured our thinking with regard to the temporal dimension of technological innovation? What impact have these models had on empirical research? How useful is this research for technology policy? 2. Temporal models As the above quotes and examples suggest, almost every analysis of time and innovation begins with the idea of technological cycles. The most widely accepted set of temporal models and indicators has to do with product and/or process cycle time. New product development cycles are the most commonly used measures. These cycles are generally defined as either the time it takes to move a new idea through various innovation stages to the market or the rate of new product introductions over time [12]. 2.1. Product cycles The dominant model posits that an industry or firm will pass through a number of distinct technological stages over time. In stage one, most new companies or sectors are characterized by significant uncertainty about market size, product design, consumer tastes, and technological constraints. This ‘‘youthful’’ state is a fluid period of experimentation with novel designs and it typically leads to one or more radical breakthroughs and a variety of designs offered by the industry. Here technological opportunities are high and the rate of technological change accelerates. With the emergence of a ‘‘dominant design’’ and well-defined markets, however, the competitive landscape changes as radical innovation gives way to incremental advance. During this ‘‘mature’’ phase of incremental change, technological advances in products slow down. As the product technology stabilizes and becomes standardized, there is a

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transition to process technological changes. Simultaneously, there is a move from competition based on higher performance to lower cost. So innovation becomes faster. Eventually, the process technology also becomes standardized. Now the pace of innovation is slow again. In the final stage, the ‘‘old age’’ of a technology features efforts to develop new designs and start the cycle again [14]. The origins of the product cycle model are important. The initial idea was very focused; it explained observations in a specific industry (automobiles) in a particular country (the US), in a defined period (the 1970s). Most empirical testing has also been limited to the US in similar industries (e.g., typewriters, televisions). Despite these constraints, the model is widely viewed as providing a universal (or close to it) explanation for industrial and technological evolution. As one study observes, ‘‘It is hard to underestimate the influence of this theory. More than any other it has been the organizing principle around which the vast majority of theoretical and empirical work on innovation has been based during the last 20 years.’’ [15]. Yet these same analysts go on to question the universality of the product life cycle approach. They assert that the theory describes a special case, one that can not be applied to all technological sectors. A growing number of experts agree. They assert that there is no reason to assume that innovation should be expected to stabilize around a single design. Some of these critics advocate alternative models assuming innovation pattern variations across sectors—including temporal differences.

2.2. Barrier– breakthrough One of the most important models put forth as an alternative to the product life cycle nonetheless maintains the ‘‘fast–slow’’ characterization of technological change. The ‘‘barrier–breakthrough’’ model shares the temporal ‘‘pendulum’’ metaphor that is central to product cycle theory [16]. But with this approach, the concept of generic technological stages (e.g., old age) is rejected in favor of the idea that the pace of technological innovation is heavily influenced by what are termed ‘‘configurationdependent’’ constraints reflecting the specific properties of particular technologies. The basic hypothesis is that the rate of technological progress tends to be faster when a technology is far from its limits, and it tends to slow down as the technology approaches these limits [17]. Technological progress is most difficult and slowest as an innovative organization (not just a firm) approaches a technological barrier. This is what is meant by a configurationdependent limit. Technological progress is easiest after such a barrier has been overcome. This is a breakthrough. One of the major signals that limits are being approached is the rising cost of R&D or any other type of organizational learning (e.g., learning by doing) per unit of technological performance improvement. In a competitive market, such a signal usually triggers a search for a new combination of performance characteristics. Here technological progress is not as predictable as in the product life cycle approach. Barriers are confronted, and some are quickly overcome, but others take more time, and still others ultimately prove to be insurmountable. The rate of technological advance at a given time usually depends on the technological ‘‘distance’’ from limits, both prior and subsequent. And there is great variation in the barrier/breakthrough pattern from one technological sector to another.

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2.3. Technological discontinuity Another approach shares with the barrier/breakthrough conceptualization a focus on obstacles or problems facing innovative organizations, but it shifts the emphasis from the barrier itself to the internal organizational dynamics of adaptation. One of the model’s advocates asserts that established firms and other organizations have difficulties responding to ‘‘discontinuities’’—the substantial changes in the set of technological competencies required to design and produce a new product [18]. This approach often takes a resource-based view of the firm or network (expanding the analysis beyond firms or industries), arguing that innovative organizations operate with different knowledge bases, problem-solving routines, etc., that arise from historical processes and thus are path dependent. Innovative organizations thus vary greatly in their ability to adjust to new technological opportunities or problems. Often they are very restricted in even understanding discontinuities. And because of path dependency, the time horizon may be very long when making an adjustment to an unfamiliar technological and/ or organizational challenge. Faced with technological discontinuities, some organizations do prove to be adaptable. One of the ways organizations enhance their ability to change in the face of discontinuities is to belong to larger, more diverse networks (e.g., strategic alliances, integrated supplier–producer chains, geographical regions, or clusters). Reducing the time it takes to deal with organizational or technological discontinuities is often cited as one of the major reasons for the explosion in the number and variety of innovation networks [19–21]. Because it adds the organizational dimension to the mix of factors involved in technological innovation, the technological discontinuity approach assumes an enormous range of innovation patterns, including those factors related to time. 2.4. Continuous change There are some analysts who reject all of the above. Here the major assertion is that while accounts of product cycles, discontinuities, and the like have interested academics, these patterns are not a common experience of managers in most innovative firms. Instead, it is argued that many firms compete by changing continuously—especially high-velocity industries (e.g., computers) with short product cycles and rapidly shifting competitive landscapes. For these industries the ability to engage in fast and relentless continuous change is a crucial capability for survival [22,23]. The organizational and technological changes that are the focus of the continuous change model highlight the development of flexible structures, featuring extensive interaction, learning, and freedom to adapt. For example, lean or agile production processes—widely credited with major increases in the pace of manufacturing time in a range of industries—rely heavily on the incremental development and improvement of just-in-time organizational arrangements that have been transferred into supply and distribution processes, R&D management, and service operations [24]. Fig. 1 compares these four innovation models in terms of their temporal implications. There appear to be substantial differences on at least three dimensions—the nature, duration, and sectoral variation in innovation speed. Note that these are highly stylized distinctions among the models. In almost every case, the advocates of a particular model have been careful to note the advantages of alternative frameworks. With very few

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Temporal Dimensions Nature of Speed of Innovation Duration of Speed of Innovation Sectoral Variation in Speed

Product Cycles

BarrierBreakthrough

Technological Discontinuity

Continuous Change

Fast-Slow Pendulum Longer (Lasting Years, Decades)

Fast-Slow Pendulum Shorter (Lasting Months, Years)

Inconsistent (Fast and Slow) Mixture (Shorter and Longer)

Consistent (Fast or Slow) Stable (Shorter or Longer)

Little Variation

Moderate Variation

High Variation

Little Variation

Fig. 1. Temporal models of technological innovation: dimensions and key characteristics.

exceptions, however, each of the models continues to be applied in relatively pure ways. For instance, advocates of the product cycle model have acknowledged the significance of technological discontinuities. It is the emergence of a discontinuity that ends one cycle and signals the beginning of another. But the idea that discontinuities might be the dominant innovation pattern is not seriously considered [14]. So life cycle models continue to be used in ways that are not greatly affected by other approaches, and the distinctions made between the different models in Fig. 1 have tended to survive. But what do we know about the relationship between time and technological innovation? What is the state of empirical research on the temporal dimension of technological change? 3. Time and innovation: empirical research One observer has noted that ‘‘for a hot topic, innovation speed has been the focus of surprisingly few scientific studies’’ [25]. He is correct. Many analysts of technological innovation appear to simply assume the inevitability and merit of ‘‘making things fast’’ [26]. The best review of the literature—from the mid-1990s—lists only about 70 studies. The great majority of these works are accounts of personal experience or case studies. Less than 20 studies are characterized as ‘‘deductive hypothesis testing.’’ So it is no wonder that this literature is characterized as not especially coherent. The research is viewed as having major levels of analysis differences (e.g., focusing on the sector, organization, or project) and lacking consensus with regard to terminology and in the measurement of variables, including speed [27]. The following discussion begins with an emphasis on those early works that engaged in deductive hypothesis testing and adds more recent research of the same caliber. The section is not intended to be exhaustive. Instead, it presents a representative overview of how the various temporal models have been applied. 3.1. Case studies and personal experience Much of what we do know empirically is derived from case studies or the first-hand experience of experts. The earlier description of the four models is almost entirely based on such evidence (e.g., semiconductors for barrier–breakthroughs, telecommunications for technological discontinuities, computers for continuous change). Especially important are the cases (e.g., typewriters, automobiles, electric lighting) underpinning the study of product cycles [14]. However, the case studies in this area of study are often very brief and

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have not managed to escape the standard limitations of such approaches. These include small sample sizes that raise questions regarding how much can be generalized from the case findings and concerns about the fact that often cases are not selected on the basis of a predetermined set of criteria but represent targets of opportunity. 3.2. Product concentrations One of the first efforts to systematically analyze cycle speed involved aggregating, over time, the total number of products innovated in specific cycles, then analyzing the changes in product concentrations in particular stages of each cycle. This line of research has found that the percentage of products identified in the first two stages of the product life cycle (usually termed introductory and growth) increased over a 50-year time frame. This finding suggests that new products were being introduced faster [28]. 3.3. Surveys of expert opinion Perhaps the first significant survey research application to have any interest in innovation speed was The Yale Survey on Industrial Research and Development. Conducted by a group of scholars in 1983–1984, this survey elicited an extensive range of responses from 650 managers who were knowledgeable about relevant technology in their businesses. Among the broad range of questions on other matters was one section asking respondents to assess, on a 1–7 scale (with 1 representing very slow, 7 representing very rapid) the rate of process and product innovation in their lines of business (sectors). Then a mean rate of innovation was calculated for each sector [29]. The slowest rates of product introduction were found in sectors such as concrete and agricultural products. The most rapid rates of product introduction were in electrical equipment, computers, telecommunications equipment, and scientific instruments [30]. At about the same time as The Yale Survey, research began that was focused on the timing of corporate utilization of knowledge from university science and applied research. This work in the late 1980s involved developing survey instruments that were completed by academic scientists, corporate managers (mostly R&D executives), and company officials in US and Japanese companies in a variety of sectors (e.g., chemicals, pharmaceuticals, electrical equipment). Early research sampled 50 Japanese and 75 American firms regarding the time elapsed from applied research to commercialization. One key finding was that while the Japanese had speed advantages in machinery, they had none in chemicals. In addition, the Japanese firms were faster at linking external technology (developed outside the innovating company) than their American counterparts, but the reverse was true for the speed of linkage for internal technology [31]. A later survey had the objective of specifying the time between the generation of academic science and subsequent innovation based on the results of that scientific effort [32]. For each surveyed firm’s new products introduced in 1975–85 that ‘‘could not have been developed without substantial delay in the absence of recent academic research,’’ data were obtained regarding the time between each scientific discovery and the first commercial introduction of a product or process. The study was limited to research occurring within 15 years of the commercialization of whatever innovation was being considered (thus, the use of the term ‘‘recent’’ research). The mean time lag in the responding industries was about 7 years,

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but there were substantial variations across sectors. The pharmaceutical industry had the shortest discovery-to-innovation elapsed time, and the petroleum industry the longest [33]. More recent survey research has been generated from the fields of management and marketing, but some of it continues to focus on linkages between knowledge and speed. Various models have been created to investigate factors that influence the time it takes new firms to bring first products to the marketplace. One such model surveyed founders, chief executive officers, and key managers and found that less technically ambitious processes that relied on relatively simpler knowledge bases (i.e., low levels of new knowledge creation, little synthesizing of existing knowledge in new ways) led to faster movement to market [34]. The idea that innovation speed is enhanced by keeping scientific and technological knowledge as simple as possible has been supported by other research—a survey of a sample of 73 small, high-technology manufacturing firms indicated that faster development of products was related to maintaining the simplicity of technical content [35]. Not surprisingly, however, the bulk of the survey research in the management literature focuses on the impact on innovation speed of factors like corporate strategy. Perhaps the most general analysis has investigated the overall orientation of business management toward the issue of fast introduction of new products [36]. In one such study it was found that US R&D, marketing, and manufacturing managers did not emphasize product speed to the same extent as their German counterparts [37]. Another survey of 30 projects in 12 British companies reported that leadership style influenced the speed of development. However, the source of the technologies used in the project (i.e., internally developed or externally acquired) moderated the link between the style of leadership and development speed [38]. A more focused examination of the strategy–speed linkage surveyed project team members (ranging from project leaders to engineering and marketing experts) in 10 large US companies. It explored innovation speed (i.e., the elapsed time between product conception and commercialization) in 75 new product development projects. Respondents were asked questions designed to compare the speed of their projects to three benchmarks. First, were their projects completed ahead of the scheduled time? Second, how fast were their projects compared to other completed projects in the firm being studied? And third, were their projects faster than similar projects of competitor companies? The responses indicated that the product innovations took about 10% less time than the time frames for the benchmarks. Different product development strategies were identified as a key reason for these differential effects on innovation speed. Thus, reliance on external sources of capabilities during the later stages (i.e., technological development) of the product cycle was related to slower innovation [39]. Similar surveys have focused on the links between strategy and organizational variables (e.g., degree of managerial support, level of project team integration) and the speed of new radical and incremental product development. A good example is a survey of more than a 100 respondents from 10 firms involved in 75 new product development projects. Respondents indicated that projects took less time when they had clear time goals, were assigned team members with longer tenure in the organization, were executed by concurrent engineering processes, etc. Respondents also indicated that some factors that sped up radical innovation (e.g., the presence of a product champion) were found to slow incremental innovation [40]. Additional survey research has generally supported the idea that organizational/ managerial variables affect speed in many ways, including having differential impacts at

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various project stages [41]. For instance, the managers involved in 95 successful and unsuccessful new product projects were surveyed and it was found that the speed of development interacted with a number of factors (e.g., a long-term view of product development, a stable project vision) to improve new product success rates [42]. Another survey tested the consequences for innovation time using two organizational models, one termed ‘‘compression’’ (squeezing together the steps involved in product innovation, such as involving suppliers and using computer-aided design) and the other called ‘‘experimental’’ (relying on real-time experience, flexibility, and improvisation). Participants in 72 product development projects at US, European, and Asian computer firms were surveyed. It was found that the experimental approach was more generally successful in increasing innovation speed across the board. The compression approach only accelerated product development in mature industry segments [43]. This research was somewhat at odds with earlier work that had identified elements of the compression model—particularly the reliance on supplier innovation—as a source of enhanced speed. For example, a study of the automobile industry in the US and Japan, involving 29 projects in 20 firms, found that Japanese reliance on intensive supplier engineering accounted for a significant fraction of their corporate advantage in product lead time [44]. Teamwork itself has been the key variable in several surveys of the temporal dimension of innovation. A study of over 100 projects in the chemical industry explored the key factors behind on-time, fast-paced projects. The major finding was that cross-functional teams were the strongest drivers of timeliness, along with a strong leader and management support [45]. Not surprisingly, team leadership characteristics have been identified as one key to project speed. But those leadership characteristics that enhance rapid project development have also been found to vary with the types of projects and type of work undertaken (i.e., the technology) [46]. At least some of the team-based characteristics associated with fast product development appear to cross-national in character. For example, a survey of electronics firms in five countries identified common US and Japanese approaches to development team diversity and integration and an early focus on known customer requirements as keys to fast development [47]. Within development teams, the relative emphasis personnel place on keeping a project on schedule (linked to perceptions of management preferences, individual specialization, etc.) has emerged as a key to the pace of projects [48]. Other team variables may also be necessary for cycle-time reduction. A study of 15 software package companies in the Washington–Baltimore metropolitan area identified the importance for product cycle-time reduction of an entrepreneurial core development team that shared a common vision of the product’s design, use, and long-term direction. Even with these characteristics, however, the survey found that these software developers had to continue to resort to crunch periods of intensive effort aimed at meeting deadlines [49]. The information processing capabilities of project teams has also attracted the attention of researchers interested in time and innovation. A study of R&D project groups from four industrial organizations (a total of 98 groups comprised of over 600 professionals) found that the performance of their task (rapid technological development) was associated with large amounts of information transferred within and outside the organization [50]. Questions have also been asked regarding the ultimate payoff of efforts to speed new product development. Here the findings are mixed. A survey of Dutch companies asked about the relationship between the intensive use of a hierarchy of acceleration methods (i.e., simplifying the development process, eliminating unnecessary steps, performing

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multiple steps at the same time) and financial performance. One of the findings was that these methods had a positive effect on speed and profitability [51]. On the other hand, a survey of one firm’s project leaders for 24 new product projects found that over a 5-year period no relationship between the time taken to develop new ideas and perceived performance. Rapid product development times were not necessarily correlated with commercial success [52]. This is part of a general trend in recent years to ask more questions about the liabilities of innovating rapidly, or at least what other factors might account for innovation success. As one study put it: Recent studies indicate that being first to market is not necessarily any better than being second, third, or even fifth. Several leading companies in the fast cycle time movement are rethinking their first-to-market strategy, and some are deliberately lengthening their cycle times [53]. Increasingly survey researchers ask whether faster innovation is always better. Thus questionnaires filled out by over 700 project managers involved in almost 700 new product development projects produced results indicating that speed to market was generally positively associated with new product success. But the responses also pointed out that market uncertainty moderated the direct effect of speed on successful innovation. Timebased strategies turned out to be more important in emerging or fast-changing markets, but not in stable ones [54]. It may also be the case that commercial success depends as much or more on compressing the cycle time between the introduction of new products as on each discrete product cycle. This is what one study calls the ‘‘new product success-tosuccess cycle time’’ [55]. Finally, researchers have explored whether there are any general patterns that can be identified in management approaches in order to improve innovation speed. At least in the early 1990s and in the case of manufacturing, the answer appeared to be a simple yes. One survey of 712 respondents in 42 US plants from three industries found that there was a common strategic approach (e.g., design for manufacturability, concurrent engineering, just-in-time practices) among fast innovators [56]. However, other survey-based studies have argued that the pattern is more involved. For instance, research indicates that different strategic options may affect different product dimensions. Thus, using crossfunctional teams appears to interact with product newness (how much of the product must be redesigned) in ways that reduce cycle time. Using a formal product development process seems to interact with product complexity (the number of product functions) to decrease cycle times. So the mix of strategy and product traits may affect overall speed [57]. Moreover, the combination of strategy and product may have different implications for the overall performance of the firm. This conclusion has been generated from a survey of firms in the automobile and computer industry in four countries. Here some product development practices (e.g., cross-functional teams, advanced design tools) are shown to interact with accelerated product development to improve corporate performance, but other practices (e.g., reverse engineering) act to limit the organizational benefits from faster product development [58]. 3.4. Sales growth and decline Another line of research has focused on fluctuations in sales as an indicator of product cycle speed. Here much of the focus is on the product category (i.e., a group of products

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that are close substitutes and fulfill a distinct niche from the consumer’s viewpoint). Examples are mostly consumer durables (e.g., televisions, refrigerators). In this research, the emphasis is on the identification of four stages of the product cycle for each set of product categories. Each of these stages is defined in terms of sales. The introduction stage is characterized by commercialization when the first product is sold. Stage two is takeoff, marked by the first dramatic, sustained sales increase. The third stage, slowdown, marks the beginning of level, slowly increasing, or slowly decreasing sales. Finally, decline features decreasing sales until the demise of the entire product category. Analysts using this approach have investigated 30 product categories and found patterns of rapid growth in sales during the early stages of the product cycle (averaging about 45% per year over 8 years). This fast growth was followed by much slower sales in the later stages of the cycle, and these later declines in sales stayed below the previous peak sales for some time [59,60]. Other research has used this approach to compare the sales fluctuation patterns of 10 consumer durables across 16 European countries. One of the major findings is that sales time-to-takeoff is very different across product classes (e.g., kitchen appliances versus entertainment products) and among countries. For example, time-to-takeoff averaged 3.3 years for Denmark and 9.3 years for Portugal. Factors such as national cultures and the different innovative capacity of various countries are advanced as explanations for these temporal differences [61]. A different research design focused on the computer industry in order to learn about sales growth and decline in a more dynamic sector. Here the study examined sales over the period between the introduction and withdrawal of desk-top computers between 1974 and 1992. One finding showed that the time it took to reach peak sales of one model of an 8-bit personal computer was 11 years, while the time to peak sales of its successor was longer (14 years) [62]. 3.5. Age of cited patents The indicator of technology product cycle time that has enjoyed the most recent popularity was developed by CHI Research, Incorporated. It defines technology cycle time (or TCT) in terms of the median age, in years, of the US patents (its own and others’) cited on the first page of an approved patent application. The front page of the application approval form lists the prior patents that the newly approved patent builds on. Because the earlier technology cited in a new patent represents prior art, the TCT can be seen to represent the cycle time between generations of technology. Thus, the smaller the TCT value, the faster the technological turnover [63,64]. Using this measure, and focusing on the most innovative companies in a number of sectors, it has been found that the overall pace of innovation has been accelerating rapidly. For example, in the aerospace sector between 1994 and 1999, the average TCT for 14 of the leading firms decreased from 10.1 to 8.4 years. In the telecommunications sector, in 1994 the TCT for 18 innovative firms averaged some 5.7 years. By 1999, the average TCT for innovations in the telecommunications sector was 5.4 years [65]. Another application of this approach has been applied to the pharmaceutical sector. Here it was found that faster TCT was positively correlated to measures of the knowledge base level (e.g., R&D intensity, number of patents) and breadth (e.g., number of strategic alliances) of firms, as well as firm size and age. Of particular interest was the finding that

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Temporal Dimensions Temporal Indicators

Temporal Model

Product Concentrations Changes over time in percentage of total products in particular product cycle stages Product Cycle

Expert Opinion Survey responses assessing pace of innovation, time from science to innovation Implied Product Cycle

Sales Growth And Decline Changes over time in product sales in particular product cycle stages Product Cycle

Age of Cited Patents Median age of patents cited in approved patent application Implied Product Cycle

Fig. 2. Approaches to measuring the temporal dimension of technological innovation.

firms that generate new knowledge internally have significantly faster technology cycle times than those that generate new knowledge from external sources [66]. Finally, the TCT measure has been used to inform public policy. The Office of Technology Policy in the US Department of Commerce has conducted an analysis of US competitiveness using the TCT indicator as a way to gauge national capabilities to rapidly integrate new knowledge and technology into products. This study compares the US, the European Union as a group, and 14 other countries in five technological sectors (health, advanced materials, automotive, information technology, and express package transportation and logistics). The general finding was that US cycle times lagged behind everyone else by between 10% and 50% over the period between 1982 and 1996. The US was especially slow in comparison with Japan—which had the fastest cycle times in each sector. Germany and the UK were found to be making especially rapid progress in shortening cycle times for advanced materials, while in the automotive sector, Taiwan had cut its cycle time in half. Both Taiwan and Korea had overtaken Japan in the information technology sector. According to this analysis, cycle times were getting shorter in all but one sector, health, where the TCT actually increased [67]. Fig. 2 summarizes the approaches to measuring the temporal dimension of technological innovation. One thing is striking about the state-of-the-art presented in this figure. To the degree that there is rigorous and systematic analysis (i.e., not based entirely on case studies or personal experience), it has been heavily dependent on the product cycle model. There does not seem to be any comparable work with the barrier–breakthrough, technological discontinuity, or continuous change models. This says a lot about the amazing attractiveness of the life cycle model. However, at least as much of the seductiveness of the product cycle can be attributed to its role as metaphor as to its ability to provide compelling empirical evidence. It is in this metaphorical role that we can identify the key public policy issue.

4. Metaphors and public policy Critics of the widespread use of the product cycle model have argued that it is important to ‘‘distinguish between descriptive metaphors and conceptual understanding’’ and to develop more ‘‘robust theories that explain the dynamics of a system’’ in the process of moving from the former to the latter [15]. This is certainly the case, but the conceptual understanding provided by metaphors should not be under-estimated, especially for the public policy process.

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Metaphors provide a way ‘‘for individuals grounded in different contexts and with different experiences to understand something intuitively through the use of imagination and symbols without the need for analysis or generalization’’ [68]. By way of metaphors, groups of people can put together what they know, both tacitly and explicitly, in new ways, and begin to communicate new knowledge. Metaphors are essential for policy-making because images are needed to translate complex phenomena into operational and actionable terminology. The relationship between metaphors and policy has been described as follows: It turns out that an awful lot of policy-making has to do with finding the appropriate metaphor. Conversely, bad policy-making almost always involves finding inappropriate metaphors. For example, it may not be appropriate to think about a ‘‘drug war,’’ with guns and assaults [69]. Unfortunately, the product cycle appears to be a bad metaphor for modern innovation policy. There are at least two good reasons for coming to this conclusion. First, there is little relationship between the product cycle model and the empirical evidence reported in most of the research on the temporal dimension of technological change. Other than some of the case study evidence, almost every other analysis reports innovation speed in terms of the entire cycle. Key factors (e.g., the shift from product to process technology) are ignored. The result is a simplistic, linear image in which the fundamental fast–slow pendulum dynamic of the product cycle is entirely lost. It is not that there is an attempt to disguise this loss of detail—the TCT measure, for example, is explicit about focusing on the time consumed in the entire product cycle. But the result is that most non-case-study research findings are much more supportive of the patterns specified by the continuous change model (e.g., consistently fast or slow innovation) than the product life cycle. Just how important are the lost temporal details? Some analysts believe they are not significant. They argue that it may be enough to know exactly what the TCT provides. The rationale is that over time most industries appear to settle into a common innovation trajectory or path that leads to a state of relative parity with regard to how fast products are developed [70]. It may be that mature industries have evolved to a state in which they innovate in a predictable innovation time band. And the concept of a technological trajectory (i.e., the co-evolution of a technology and organization along a pathway according to a paradigm or regime [71]) is in itself a very useful metaphor for helping come to grips with innovation speed. But what about the relative time consumed by radical innovations in early cycle stages versus incremental innovations later on? What about newer sectors, many of which may consist of fused elements of older technologies? At what point do they manifest an established innovation trajectory or path characterized by relatively predictable speed? The rather sanguine view of a relatively stable trajectory ignores alternative descriptions of innovation patterns, particularly the discontinuous change model, which suggests technological pathways are routinely characterized by barriers and that overcoming them produces a bumpy or oscillating effect. There is substantial evidence that a number of conditions can disrupt an existing trajectory. Consensus concerning the next series of incremental innovations can erode, leading to a shared perception that a technology has ‘‘run out its string.’’ The entry of new competitors with new knowledge and capabilities may make it possible, or necessary, to alter the established technological path. The integration of new technological systems or subsystems (e.g., information and communication

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technology components) may fundamentally alter the product or process. Broad change in the technological context (e.g., market changes, new public policies) may also alter continued innovation down an existing path [72]. It is important to note that each of these discontinuities has major implications for the speed of innovation, but in each case it is not certain whether innovation speed is accelerated or slowed. The second liability of the product cycle metaphor is that it does not reflect the emergence of complex products and their even more complex production systems. Instead, as Hobday says: ‘‘Although seldom noted, the conventional model [the product life cycle] is intimately linked to the production paradigm of mass market commodity goods’’ [73]. The model continues to represent the simple product and process innovations of the immediate post-World War II era. Thus the product cycle model has difficulty incorporating the characteristics of complex technological innovation such as the large number of interconnected systems and components, the involvement of many collaborating organizations, and the emergence of unexpected organizational and technological properties. The history of the jet engine provides a good example of these factors. The jet engine in the early 1930s was simple. It was composed of a compressor, fuel and ignition device, turbine, and exhaust nozzle. It had just one moving part, the compressor–turbine combination. Yet over time the jet engine steadily became more complex. Commercial and military interests exerted constant pressure to overcome performance limits and handle exceptional operational situations. Often the resulting improvements were achieved by adding new subsystems. But these require even more subsystems to monitor and control them, and so on. The result is that modern jet engines have more than 20,000 parts. And they are 30–50 times more powerful than the early models [74]. Innovating and managing such complex technology requires new organizational forms. Increasingly, these technologies are innovated by complex networks—those linked organizations (e.g., corporate, university, or government labs) that create, acquire, and integrate the required knowledge and skills. Organizational learning is the key. Accessing tacit knowledge (i.e., experience-based, unwritten know-how) and integrating it with codified knowledge is a particular strength of many of these networks. Examples include strategic alliances, joint ventures, and other forms of inter-organizational collaboration. As with many other sectors, as aircraft technology became more complex, so did learning-based aircraft networks. Between the mid-1930s and 1945, aircraft technology became standardized and there was little networking in the aircraft industry. In the postWorld War II period new technological trajectories proliferated and engines began to be linked to specific market segments (e.g., turboprops to cargo, jets to military, piston/ propeller to business). Eventually, very specific niche markets with highly specialized and very complex aircraft engine networks emerged [75]. Frameworks like barrier–breakthrough, technological discontinuity, and continuous change have been formulated in more recent years and are in large part a response to the kind of increased organizational and technological complexity manifested in the jet engine trajectories. As a result, the metaphors they convey hold the promise of being of more use to policy makers. In an era of complex technologies, and that will surely be the dominant characteristic of the early part of the twenty-first century, public policy will need to facilitate learning and be ever more adaptable. Public policies aimed at facilitating complex innovation will need

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to be as learning-based, flexible, and fast as possible to stay relevant for the innovation process itself. Perhaps part of the answer is to develop multiple categories of temporal indicators. This would involve building on the existing work focused on product speed and expanding it. For instance, the simultaneous development of three sets of temporal metrics has been suggested:

  

Process technology speed, measured by capital equipment obsolescence rates. Product technology speed, measured by rates of new product introduction or intervals between new product generations. Organizational innovation speed, measured by rates of change in organizational structures, routines, and capabilities.

For all three categories, not only the mean speed should be considered, but also its variance [76]. This typology seems to have great potential for enhancing policy-making. We need to know a lot more about the speed of process technological innovation. Yet generating such knowledge will not be easy because process technologies have intimate linkages to both product technologies and organizational factors. The relationships between process and product innovation have been expanding for several decades. Accelerated development times mean that improvement in areas such as the quality and reliability of products increasingly depends on concurrent production changes. Less obvious but equally important has been the organizational–process relationship in which many sources of production speed are as much the result of organizational changes (e.g., just-in-time delivery systems) as technological ones. It may be that policy makers will simply have to get used to these inter-relationships, but only additional research will tell [77]. Perhaps even more urgency surrounds the need to include organizational variables in the analysis of the speed of technological evolution. Indeed, much of the difference in innovation speed between the public and private sectors may be traced to organizational factors. For instance, companies may be better organized than public agencies (e.g., more highly networked) and thus more able to handle exactly the kinds of discontinuities and other forms of complex, nonlinear change highlighted by the temporal models posed as alternatives to the simpler product cycle [78]. A focus on the organizational aspects of innovation speed also reinforces governmental efforts to develop better ‘‘network indicators,’’ and includes in this effort the dimension of innovation network speed. As noted above, the link between networks and innovation time has been mostly neglected to date except for advocates of the technological discontinuity model. It is common for the literature on innovation network indicators to list ‘‘shortening the innovation cycle’’ as one of the major motives for creating partnerships [79]. But only a few studies, mostly based in survey research, explicitly link enhanced innovation speed to network relationships (e.g., strategic alliances, cooperation among producers, suppliers, and customers) [80,81]. This is a striking omission in an era characterized by complex product and process innovations that are becoming increasingly multi-capability and multi-organizational. Today only a small minority of firms and other organizations innovate in isolation. Data based on the European Community’s Innovation Survey show that most innovations

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involve several organizations [1]. Thus some observers are now arguing that the network is replacing the firm as the dominant actor in an increasingly knowledge-based economy [8]. Many of these networks generate substantial levels of trust and reciprocity among member organizations. Trusting, reciprocal relationships seem to help innovation networks to succeed in a number of ways—including some that are extremely valuable for rapidly innovating knowledge-intensive technologies. For example, the diffusion of credible knowledge among network members is much faster and more accurate when characterized by high levels of confidence in source integrity and solid faith that the knowledge transferred will be returned in kind. Thus, trust appears to lead to patterns of behavior that can quickly increase the productivity of knowledge [84–86]. It is no coincidence that trust-based network relationships are found mostly in complex technological sectors. These are the technologies that require the integration of the widest range of knowledge bases and capabilities. Keeping pace with the complexity of technological progress requires that networks repeatedly learn about, integrate, and apply a wide variety of types of knowledge and know-how under intense time pressures. Increasing pressures to capture the benefits of new knowledge demand streamlined ways of undertaking sophisticated collaborative inquiry, self-reflection, and scanning capabilities, as network members try to steer innovation in a context of pervasive uncertainties. To meet the challenge of technological acceleration, firms and other organizations must learn tacit (e.g., unwritten know-how) as well as explicit knowledge faster and more effectively because the most successful innovation networks are also fast organizational learning systems. Trust seems to enhance fast learning [87]. As one study puts it, by creating and maintaining the connectability of social, economic, and technical interactions in network organizations, trust absorbs complexity [88]. Ultimately, the advantage provided by innovation networks with high levels of trust is that they are adaptable in their learning processes. Few of their organizational learning approaches are easy, inexpensive, or risk-free. Yet once patterns of learning are established that make repeated complex innovation possible, a network may have a sustainable competitive advantage. The dynamic capabilities that result from rapid network learning are difficult for competitors to replicate, precisely because they are constantly evolving and emerging. Thus, the overall advantages of innovation networks seem to be substantial. But what does the proliferation of trust-based innovation networks have to do with public policy? The answer is that governments in almost all developed countries have become involved in these collaborative activities, primarily to increase the speed of innovation. For example, a recent study conducted by the US National Academy of Sciences is explicit about the focus on innovation speed as a reason for the federal government to cooperate with firms. It cites ‘‘accelerating the development of new technologies from idea to market’’ as the central public policy objective [89]. Networks of high-technology firms and government agencies are now assumed to be associated with rapid rates of innovation (e.g., the role of the US Defense Advanced Research Projects Agency, DARPA, in the successful semiconductor manufacturing technology consortium, Sematech), and faster innovation is assumed to be responsible for sustained economic expansion [90]. In particular, public–private collaboration is now viewed by many government organizations as enhancing the rapid transfer of innovation best practices that can be incorporated into future public policies. Perhaps the most popular example of such a practice is government–industry cooperation in the formulation of technology roadmaps.

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A roadmap is a sophisticated intelligence-gathering, scanning, and search initiative designed to represent a collective vision of technological futures [91–93]. The attractiveness of this type of exercise is that it moves away from developing technological strategies for fixed environments to creating a technique that enables organizations to adapt to dynamic changes in technology. The goal is to anticipate technological innovations ‘‘by mapping them on a temporal scale’’ [94]. The key word here is anticipate, not predict. Indeed, experienced participants in roadmap exercises would likely be shocked if the future actually unfolded as forecast. They have come to recognize that our lack of knowledge about the future pathways and outcomes of technological innovation is not due to a lack of diligence on our part. Rather, our surprise is a natural outcome of the rapid process of technological change [95]. 5. Making public policy in a Moore’s Law world How do you make public policy on a technological frontier that is moving very quickly? One answer is to recognize that innovation policy is fraught with uncertainty. There is no way to be assured of successful policy in advance of trying it. Like the innovation of complex technologies, the formulation of successful policies is unknowable in a detailed sense [96]. Thus, specific policy prescriptions developed in the absence of the specific context of innovation are as dangerous as they are tempting. Instead, it is important to think of innovation policy in broad conceptual ways because specific policy problems and issues can only be addressed within the context of agreed upon general concepts (e.g., the patterns of network-technology co-evolution) [97]. For the public sector, this requires a radical change in orientation: Adaptation, co-evolution, agility, and improvisation—all things that come hard to government. My argument is that we have entered the next major industrial revolution. We are witnessing a major change in how we manufacture, where we manufacture, and even if we choose to manufacture (substituting information for things and extracting economic value from bits, not atoms). Most of what we call government was set up in the last 30–50 years. We have entered the 21st century with outmoded bureaucratic structures firmly in place—structures designed to deal with the first industrial revolution and its aftermath, not proactively with the emerging knowledge economy. Attempts to ‘‘reinvent’’ government have focused mostly on what we already do, not on fundamentally changing how we think [98]. How might we rethink the linkage between the temporal dimension of technological innovation and public policy? We would do well to take into account Nelson’s idea of ‘‘appreciative theorizing.’’ As he explains this concept, understanding ‘‘quite complex causal arguments’’ depends to some degree on theorizing based in the ‘‘stories’’ told by those who appreciate the details (in this case, scientists, engineers, and other innovation professionals). So we need to keep as many of the details in as possible. For him the world is more complex than most theory has let on, and complexity matters a great deal in technological innovation. For example, he argues that we can not understand the process of generating knowledge if we homogenize the process into a ‘‘sausage machine’’ called R&D. Instead, we need to think about what knowledge really consists of and how it is actually generated and transmitted [99].

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The dominance of the product cycle model may be evidence of another sausage machine at work. We may risk having the details of the temporal dimension of technological innovation eliminated or greatly oversimplified if multiple indicators are not developed and refined. In such a situation, policy making is likely to suffer. References [1] Lundvall B, Borras S. The globalising learning economy: implications for innovation policy. Luxembourg: Office for Official Publications of the European Communities; 1999 (p. 30). [2] Popper SW. Technological change and the challenges for 21st century governance. In: Teich AH, Nelson SD, Lita SJ, Hunt AE, editors. AAAS science and technology policy yearbook. Washington: American Association for the Advancement of Science; 2003. p. 86. [3] Moore GE. Cramming more components onto integrated circuits. Electronics 1965;38(8):114–7. [4] Gilder G. Metcalfe’s law and legacy. Forbes 1993;ASAP:158–66. [5] Griffin A. PDMA research on new product development practices: updating trends and benchmarking best practices. J Prod Innovat Manage 1997;14(6):429–58. [6] Tassey G. R&D trends in the US economy: strategies and policy implications. Washington, DC: National Institute of Standards and Technology Planning Report 99-62, US Department of Commerce; 1999 (p. ES-2). [7] Vandenbosch M, Clift T. Dramatically reducing cycle times through flash development. Long Range Plann 2002;35(6):567–89. [8] Skolnikoff EB. International governance in a technological age. In: De la Mothe J, editor. Science, technology, and governance. New York: Continuum Press; 2001. p. 123. [9] Chakravorti B. The role of adoption networks in the success of innovations: a strategic perspective. Technol Soc 2004;26(2–3):469–82. [10] Chakravorti B. The slow pace of fast change: bringing innovations to market in a connected world. Cambridge: Harvard Business School Press; 2003. [11] Kodama F. Technology fusion and the new R&D. Harvard Bus Rev 1992;70(4):70–8. [12] Levinthal DA. The slow pace of rapid technological change: gradualism and punctuation in technological change. Ind Corporate Change 1998;7(2):217–47. [14] Utterback JM. Mastering the dynamics of innovation: how companies can seize opportunities in the face of technological change. Cambridge: Harvard University Press; 1994 (p. xiii–xxix). [15] Windrum P, Birchenhall C. Is product life cycle theory a special case? Dominant designs and the emergence of market niches through coevolutionary-learning. Struct Change Econ Dyn 1998;9(1):110. [16] Ayres RU. Barriers and breakthroughs: an expanding frontiers model of the technology life cycle. Technovation 1988;7:87–115. [17] Ayres RU. Toward a non-linear dynamics of technological progress. J Econ Behav Organ 1994;24: 35–69. [18] Ehrnberg E. On the definition and measurement of technological discontinuities. Technovation 1995;15(7):437–52. [19] Ehrnberg E, Jacobsson S. Managing technological discontinuities—a tentative framework. Int J Technol Manage 1996;11(3/4):460–2. [20] Lambe CJ, Spekman RE. Alliances, external technology acquisition and discontinuous technological change. J Prod Innovat Manage 1997;14(2):102–16. [21] Tyre MJ, Orlikowski WJ. Windows of opportunity: temporal patterns of technological adaptation in organizations. Organ Sci 1994;5(1):98–118. [22] Brown SL, Eisenhardt KM. The art of continuous change: linking complexity theory and time-paced evolution in relentlessly shifting organizations. Admin Sci Quart 1997;42(1):1–34. [23] Smeds R. Radical change through incremental innovations: generic principles and cultural differences in evolution management. Int J Technol Manage 1997;14(1):146–62. [24] Bessant J, Caffyn S. High-involvement innovation through continuous improvement. Int J Technol Manage 1997;14(1):7–13. [25] Roddick H. True or false: faster innovation is better? Working research. New York: Lubin School of Business, Pace University; 2003 (p. 1). [26] Schmenner RW. The merit of making things fast. Sloan Manage Rev 1988;30(1):11–7.

ARTICLE IN PRESS R.W. Rycroft / Technology in Society 28 (2006) 281–301

299

[27] Kessler EH, Chakrabarti AK. A conceptual model of context, antecedents, and outcomes in innovation speed. Acad Manage Rev 1996;21(4):1144–54. [28] Qualls WR, Olshavsky RW, Michaels RE. Shortening of the PLC—an empirical test. J Market 1981;45(4):76–80. [29] Klevorick AK, Levin RC, Nelson RR, Winter SG. On the sources and significance of interindustry differences in technological opportunity. Res Policy 1995;24(2):203. [30] Levin R, Klevorick AK, Nelson RR, Winter SG. Appropriating the returns from industrial research and development. Brookings Pap Econ Activity 1987;3:812–3. [31] Mansfield E. The speed and cost of industrial innovation in Japan and the United States: external versus internal technology. Manage Sci 1988;34(10):1157–69. [32] Mansfield E. Academic research and industrial innovation. Res Policy 1991;20(1):1–12. [33] Mansfield E. Academic research underlying industrial innovations: sources, characteristics, and financing. Rev Econ Stat 1995;77(1):55–6. [34] Schoonhoven CB, Eisenhardt KM, Lyman K. Speeding products to market: waiting time to first product introduction in new firms. Admin Sci Quart 1990;35(Special Issue):177–207. [35] Ali A, Krapfel Jr R, LaBahn D. Product innovativeness and entry strategy: impact on cycle time and breakeven time. J Prod Innovat Manage 1995;12(1):54–69. [36] Menon A, Chowdhury J, Lukas BA. Antecedents and outcomes of new product development speed: an interdisciplinary conceptual framework. Ind Market Manage 2002:317–28. [37] Gupta AK, Brockhoff K, Weisenfeld U. Making trade-offs in the new product development process: a German/US comparison. J Prod Innovat Manage 1992;9(1):11–8. [38] McDonough III EF, Barczak G. Speeding up new product development: the effects of leadership style and source of technology. J Prod Innovat Manage 1991;8(3):203–11. [39] Kessler EH, Bierly III PE. Is faster really better? An empirical test of the implications of innovation speed. IEEE Trans Eng Manage 2002;49(1):1–12. [40] Kessler EH, Chakrabarti AK. Speeding up the pace of new product development. J Prod Innovat Manage 1999;16(3):231–47. [41] Young CS, Samson D. Team management and project speed in process industries. Department of Management Working Paper Series, University of Melbourne, Australia, 2003. [42] Lynn GS, Abel KD, Valentine WS, Wright RC. Key factors in increasing speed to market and improving new product success rates. Ind Mark Manage 1999;28(4):319–26. [43] Eisenhardt KM, Tabrizi BN. Accelerating adaptive processes: product innovation in the global computer industry. Admin Sci Quart 1995;40:84–110. [44] Clark KB. Product scope and product performance: the effect of parts strategy and supplier involvement on product development. Manage Sci 1989;35(10):1247–63. [45] Cooper RG, Kleinschmidt EJ. Determinants of timeliness in product development. J Prod Innovat Manage 1994;11(5):381–96. [46] McDonough III EF. Faster new product development: investigating the effects of technology and characteristics of the project leader and team. J Prod Innovat Manage 1993;10(3):241–50. [47] Birnbaum-More PH. New product development time: a cross-national study. Center for Innovation Management Studies, Lehigh University, November 1993. p. 1–3. [48] Rosenau Jr MD. Schedule emphasis of new product development personnel. J Prod Innovat Manage 1989;6(4):282–8. [49] Carmel E. Cycle time in packaged software firms. J Prod Innovat Manage 1995;12(2):110–23. [50] Keller RT. Technology-information processing fit and the performance of R&D project groups: a test of contingency theory. Acad Manage J 1994;7(1):167–79. [51] Nijssen EJ, Arbouw RL, Commandeur HR. Accelerating new product development: a preliminary test of a hierarchy of implementation. J Prod Innovat Manage 1995;12(2):99–109. [52] Meyer MH, Utterbeck JM. Product development cycle time and commercial success. IEEE Trans Eng Manage 1995;42(4):297–304. [53] Lambert D, Slater SF. First, fast, and on time: the path to success, or is it? J Prod Innovat Manage 1999;16(5):427. [54] Chen J, Reilly RR, Lynn GS. The role of uncertainty in speed-to-market and new product success. Center for Technology Management 2004, Research Working Paper No. 12/21, Wesley J. Howe School of Technology Management, Stevens Institute of Technology. [55] Flint DJ. Compressing new product success-to-success cycle time: deep customer value understanding and idea generation. Ind Mark Manage 2002;31(4):305–15.

ARTICLE IN PRESS 300

R.W. Rycroft / Technology in Society 28 (2006) 281–301

[56] Flynn BB. The role of quality management and organizational infrastructure in fast cycle/time-driven innovation. Center for Innovation Management Research, Lehigh University; 1993. [57] Griffin A. The effect of project and process characteristics on product development cycle time. J Market Res 1997;34(1):24–5. [58] Ittner CD, Larker DF. Product development cycle time and organizational performance. J Market Res 1997;34(1):13. [59] Golder PN, Tellis GJ. Growing, growing, gone: cascades, diffusion, and turning points in the product life cycle. Market Sci 2004;23(2):207–18. [60] Tellis GJ, Golder PN. First to market, first to fail? The real causes of enduring market leadership. Sloan Manage Rev 1996;37(2):65–75. [61] Tellis GJ, Stremersch S, Yin E. The international takeoff of new products: the role of economics, culture, and country innovativeness. Market Sci 2003;22(2):188–208. [62] Bayus BL. An analysis of product lifetimes in a technologically dynamic industry. Manage Sci 1998;44(6):763–75. [63] Waters RC, Kayal AA. An empirical evaluation of the technology cycle time indicator as a measure of the pace of technological progress in superconductor technology. IEEE Trans Eng Manage 1999;46(2): 127–31. [64] Kayal AA. Measuring the pace of technological progress: implications for technological forecasting. Technol Forecast Soc Change 1999;March:237–45. [65] Buderi R. Companies squeeze the patent pipeline. Technol Rev 2000;March–April:82–93. [66] Bierly P, Chakrabarti A. Determinants of technology cycle time in the US pharmaceutical industry. R&D Manage 1996;26(2):115–26. [67] Albert MB, Yoshida PG, van Opstal D. The new innovators: global patenting trends in five sectors. Washington, DC: Office of Technology Policy, US Department of Commerce; 1998 (p. 5–12). [68] Nonaka I. The knowledge-creating company. Harvard Bus Rev 1991;69(6):100. [69] Waldrop WM. Complexity: the emerging science at the edge of order and chaos. New York: Simon and Schuster; 1991 (p. 334). [70] Burdick D. PLM and innovation: a strategic imperative or oxymoron? Reno, NV: Collaborative Visions, Inc.; 2004 (p. 2). [71] Anderson B. The evolution of technological trajectories 1890–1990. Struct Change Econ Dyn 1998;9(1):5–34. [72] Rycroft RW, Kash DE. Managing complex networks—key to 21st century innovation success. Res–Technol Manage 1999;May–June:15–7. [73] Hobday M. Product complexity, innovation, and industrial organisation, Working Paper No. 52, Complex Product Systems Program, Science Policy Research Unit, University of Sussex, United Kingdom, June 1998. p. 14. [74] Arthur WB. Why do things become more complex? Sci Am 1993;268(5):144. [75] Frenken K. A complexity approach to innovation networks: the case of the aircraft industry. Res Policy 2000;February:257–72. [76] Fine CH. Industry clockspeed and competency chain design: an introductory essay. Paper produced for the proceedings of the 1996 manufacturing and service operations management conference, Dartmouth College, Hanover, NH, 1996. p. 2–3. [77] Cannell W. Conclusion: technology management in the European Union and its policy implications. In: Cannell W, Dankbaar B, editors. Technology management and public policy in the European Union. London: Oxford University Press; 1996. p. 162. [78] Rejeski DW. S&T challenges in the 21st Century: strategy and tempo. Foresight and governance project: Woodrow Wilson International Center for Scholars, Washington, DC, 2002. p. 4–5. [79] Hagedoorn J. Inter-firm R&D partnership—an overview of major trends and patterns since 1960. In: Jankowski JE, Link AN, Vonortas NS, editors. Strategic research partnerships: proceedings from an NSF workshop. Washington, DC: National Science Foundation; 2001. p. 6. [80] Hierman A, Clarysse B. Do intangible assets and pre-funding R&D. Efforts matter for innovation speed in start-ups? Vlerick Leuven Gent Working Paper 2004/04. Belgium: Ghent University; 2004. [81] Deeds DL, Hill CWL. Strategic alliances and the rate of new product development: an empirical study of biotechnology firms. J Bus Venturing 1996;January:41–55. [84] Zaheer A, McEviley Jr B, Perrone V. Does trust matter? Exploring the effects of interorganizational and impersonal trust on performance. Organ Sci 1998;9(2):141–59. [85] Levin DC, Cross R. The strength of weak ties you can trust. Manage Sci 2004;50(11):1477–90.

ARTICLE IN PRESS R.W. Rycroft / Technology in Society 28 (2006) 281–301

301

[86] Hanson N. Competition, trust, and reciprocity in the development of innovative regional milieu. Pap Reg Sci 1992;71(2):103–4. [87] Janowica M, Noorderhaven N. The role of trust in interorganizational learning in joint ventures. Discussion Paper No. 2002-119. The Netherlands: Department of Organization and Strategy, Tilberg University; 2002. p. 6. [88] Lane C, Bachmann R. The social construction of trust: supplier–purchaser relations in Britain and Germany. Organ Stud 1996;17(3):367–8. [89] Wessner CH. Government–industry partnerships for development of new technologies. Washington, DC: National Academy Press; 2002 (p. 1). [90] Jorgenson D, Stiroh K. Raising the speed limit: US economic growth in the information age. Brookings Pap Econ Activity 2000;1:125–211. [91] Probert D, Shehabuddeen N. Technology road mapping: the issues of mapping technological change. Int J Technol Manage 1999;17(6):646–61. [92] Koen PA. Technology maps: choosing the right path. Eng Manage J 1997;9(4):7–11. [93] Kostoff RN, Schaller RR. Science and technology roadmaps. IEEE Trans Eng Manage 2001;48(2):132–43. [94] Duckles JM, Coyle EJ. Purdue’s center for technology roadmapping: a resource for research and education in technology roadmapping. Lafayette, IN: Purdue University; 2002 (p. 1). [95] Popper SW. Technological change and the Challenges for 21st century governance. In: Teich AH, Nelson SD, Lita SJ, Hunt AE, editors. AAAS science and technology policy yearbook. Washington: American Association for the Advancement of Science; 2003. p. 94. [96] Durlauf S. What should policymakers know about economic complexity? Wash Quart 1998;21(1):164. [97] Smith K. Innovation policy in an evolutionary context. In: Saviotti P, Metcalfe JS, editors. Evolutionary theories of economic and technological change: Present status and future prospects, Reading, PA: Harwood Academic Publishers; 1991. p. 257. [98] Rejeski D. Making policy in a Moore’s Law world. Ubiquity 2003;4(42):3. [99] Nelson RR. Recent evolutionary thinking about economic change. J Econ Lit 1995;33(1):86–95.

Further reading [13] Stalk Jr G, Hout TM. Competing against time: how time-based competition is reshaping global markets. New York: Free Press; 1990 (p. 188–91). [82] Belussi F, Arcangeli F. A typology of flexible and evolutionary firms. Res Policy 1998;27(4):145. [83] Inkpen AC. Creating knowledge through collaboration. Calif Manage Rev 1996;39(1):123–4. Robert Rycroft is Professor of International Science and Technology Policy and International Affairs at the Elliott School of International Affairs, The George Washington University. He holds a PhD in political science from the University of Oklahoma, and previously was on the faculty of the Program in Technology, Modernization, and International Studies in the Graduate School of International Studies, the University of Denver. Dr. Rycroft’s research interests focus on linking the sciences of complexity to international science and technology policy. He is the author or coauthor of over one hundred publications, including nine coauthored books. The most recent book is The Complexity Challenge: Technological Innovation for the 21st Century (with Don E. Kash). Other books include The Acid Rain Controversy, The Risk Professionals, Energy in the Global Arena, and US Energy Policy.