NISTIR 6917
Different Timelines for Different Technologies:
Evidence from the Advanced Technology Program

III. Industry and Firm Differences in Innovation:
Analytical Models in the Literature

Section III describes the models drawn from the economics literature that serve as the framework for the study.

A. The Innovation Life-Cycle Model

Over the past few decades, several variations of the industrial innovation life-cycle model have evolved in the literature of evolutionary economics and the economics of innovation. In the context of evolutionary economics, innovation is a key force in economic competition.⁽¹⁾ Patterns of innovation are characterized by the degree of technological opportunities and the ability to obtain appropriate returns from R&D, as well as characteristics of the knowledge base. Innovation life-cycle models shed light on differences in the rate of innovation activity and type of commercialization strategies commonly observed for different technologies and sectors and different innovation stages. As will be apparent in this study, these distinctions are useful in assessing commercialization patterns and progress of ATP-funded projects.

Drawing on Abernathy and Utterback (1978) and Utterback (1994), we describe three phases of industrial innovation that correspond to major transformations in characteristics of industrial innovation, market structure, and competition over time. Radically new product lines and emerging industrial sectors accompany the early (fluid) stages of the innovation cycle. In this early, fluid phase, innovation is intense, driven by the rapid entry of new, small, science-based, entrepreneurial entrants who are ready to experiment with highly uncertain new business opportunities. Such firms initially compete by delivering highly customized, essentially service products to a small group of customers. Then in the intermediate stage (the growth phase), output expands, aided by less technical uncertainty and the emergence of standards. The number of firms diminishes. In the third, mature stage, capital intensity and investment requirements increase, becoming significant barriers to the entry of new firms. Competition is now driven mostly by cost reductions, aided by process innovation, technical standards, and economies of scale. In this mature phase, a few companies dominate major markets; innovation aims at new processes; volume and cost are key drivers; and process changes and disruptive new technologies are costly, ultimately causing the pace of change to slow.

Table 1 summarizes the significant characteristics of innovation and market competition as they evolve over the three phases.

Utterback (1994) notes that the mature phase is not the end of an industry’s history. Evolution often continues in the form of waves of innovation and change. Radically new innovations may emerge from within or from outside the industry—or perhaps through collaborative activity across industries. Nevertheless, the base of firms may be smaller in subsequent waves of innovation in a given industry than it is in brand-new industries. In the subsequent waves, markets become better defined, and established firms have distribution channels in place that provide significant barriers to radical innovation or reform of the industry.

Pavitt’s taxonomy of industry trajectories complements the life-cycle approach. Pavitt (1984) identifies four types of technology trajectories, shown in Table 2.

Pavitt’s model suggests that early-stage, science-based industrial sectors, such as biotechnology and software/information systems, are characterized by an emphasis on new products with improved performance, few industry-wide standards, and high entry of small innovative firms. For example, biotechnology companies target emerging or as yet practically non-existent markets. Many biotechnology companies firms operate as adjuncts to universities. Information technology companies face different market constraints, but also have wider opportunities to serve a more diverse set of customers and industries.

Intermediate-stage science-based sectors, such as electronics and chemicals (for instance, pharmaceuticals), have entered their growth phase. Product innovation is still prevalent in the more science-based, large chemicals and electronics firms, with an evolving emphasis towards cost as well as performance.

Supplier-dominated and scale-intensive sectors have passed beyond a science-based innovation to production intensity. Firms in these sectors often have a price-sensitive end user, so the focus of innovation turns to cost cutting. In production-intensive industries such as automobile and aircraft manufacturing and petroleum refining, innovation places more emphasis on process and cost; larger, older firms primarily emphasize incremental innovation. One perceives a reluctance to pursue new markets. Industries based on assembly or fabrication (such as manufacturing) or other continuous processes (such as steel) aim at process technologies. Nevertheless, radical innovations may still emerge from outside the industry or from developers of new process tools. Multi-disciplinary, inter-industry consortia provide a mechanism for introducing new technologies to older companies and sectors.

B. Extensions of the Model

The life-cycle model of industry evolution illuminates broad economic and policy issues facing R&D-performing firms. For example, a better understanding of the technology and market environments at different stages of the innovation cycle has been useful in devising theories that describe different financing, investment, and organizational features at each stage (Auster, 1992). Of particular interest to ATP is the role of collaborative R&D as a key organizational and financing tool.

Cooperative activities offer opportunities to overcome limitations in resources (human resources, financial, fixed capital, managerial, technical and marketing) as needed at any stage of the innovation life cycle (Rothwell and Dogson, 1991). They include subcontracting, licensing, and R&D alliances and joint ventures, all features of ATP projects. (In some studies, the term “joint ventures” refers exclusively to equity-based alliances, rather than to more flexible agreements based on contracts or more informal arrangements. The official ATP definition of a joint venture does not involve any equity structure, but rather a simple contractual agreement for the purposes of accomplishing R&D goals.)⁽²⁾

The motives, structure, and performance of these linkages and collaborations are expected to differ over time and over the life-cycle of a given firm or industry, as well as across technology sectors.⁽³⁾ Vertical partnerships of users and suppliers and horizontal alliances that involve organizations in the same industry, potentially even competitors, tend to have different business objectives for their R&D collaborations.

Vonortas (1997) and Audretsch (2001) both note the strong incentives for small firms, in the fluid phase of the innovation life cycle, to seek R&D partners as a means of dealing with technological risks and with market access to rapidly changing markets. Vonortas suggests this might be more common with smaller, vertically structured joint ventures, where individual members can protect their own intellectual property in their component product innovations, rather than with horizontal R&D ventures aimed at process innovations, which are difficult to protect. For relatively mature firms, Vonortas notes, consortia are more suited to cost-reducing process innovations of generic use to a variety of member firms than they are to product innovations—and are especially suited to industries with a slow pace of technological change. Alternatively, strategic partnerships between small/new firms and larger/mature firms bring together the complementary resources needed for mature industries to innovate and diversify.

The innovation life-cycle literature focuses on the traditional assembly-line view of U.S. industry (Vonortas, 1997). Nevertheless, there are linkages to service applications as a strategy for commercializing technologies at an early stage through highly customized products.

C. Applications to the ATP Portfolio

The life-cycle framework serves as a roadmap for empirically examining the actual process of innovation for high-risk, enabling technologies such as ATP funds within their technological, business, and economic environments. Analysis of actual data helps untangle the effects of different markets and technological environments. For example, given the rapid pace of innovation in biotechnology and information technology firms, it is apparent that these are science-based firms addressing emerging industries. However, even these two technology areas differ in the way their target markets operate. Many biotechnology projects target markets that are still nearly non-existent, although many of these potential markets will involve delivery of health care services. These biotech projects also face major regulatory hurdles. Information technologies, on the other hand, target somewhat more established, less treacherous but highly diversified markets. Many IT applications involve delivery of fast-to-market service applications to varied service sectors. In general, we expect commercialization to be slower for a process innovation in a mature manufacturing industry. However, cooperative activity with different technology suppliers, and the right combination of financial and organizational backing from key customers, could speed it up.

The stylized models are presented in terms of firms and industries, not individual plants or company locations. The BRS database, on the other hand, is comprised of data from establishments directly involved in ATP-funded projects. Nevertheless, R&D projects are affected by company-wide strategic and resource considerations. The distinction between establishments and firms is not relevant for the large proportion of small firms and start-ups funded by the ATP.

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1. For example, see Breschi et al. (2000).

2. For an introduction to the literature on research partnerships, see Hagerdoon et al. (2000).

3. For example, see Hagerdoon (1993).

Return to Table of Contents or go to IV. Summary Profile

Date created: March 4, 2003
Last updated: April 12, 2005

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