Innovation, Technology and Hypercompetition

Review and Synthesis

Hans-Werner Gottinger

• Publisher: Taylor & Francis
• Year: 2013
• eBook ISBN: 9781134147243
• Edition: 1
• Topic: Business
• Subtopic: Business generale
• Index: Business

In an increasingly technologically-led century the striking pattern emerging in firms' innovative activities is their competition for a technological leadership position in situations best described as races. A 'race' is an interactive pattern characterized by firms constantly trying to get ahead of their rivals, or trying not to fall too far behind. In high technology industries, where customers are willing to pay a premium for advanced technology, leadership translates into increasing returns in the market through positive network externalities.

Innovation, Technology and Hypercompetition synthesizes and unifies the various methodological approaches for the industry-specific analysis of fast changing competitive positions driven by relentless innovation (hypercompetition). Game-theoretic and agent-based tools are applied to competitive industries in various market settings and in a global context. Rivalry of this sort is seen to extend to the catching up and forging ahead of regions and nations.

In this revealing volume, Hans-Werner Gottinger brings his expert eye to this issue and employs various tools from economic theory to attain this end. He provides the behavioural foundations for what is driving globalization, in this, a volume of interest to academic economists, legal experts, management consultants and practitioners alike.

1 Taxonomy of technological racing

Technical leadership produces high-margin products, wins competitive battles and creates new markets.

General Electric Annual Report 2003

1.1 Introduction

This chapter tracks the evolution of a cross-section of technologies in a large span of high-technology industries embracing computers, communication, pharmaceuticals and biotechnology. It outlines that strategic interactions between the firms play a substantial role in determining firm-level and industry-level technological evolution. We can identify several races supported by selected cases across industries, each of which is the result of a subset of firms jockeying for a position either as a race leader or for a position not too far behind the leader.

The identification and interpretation of the races relies on the fact that different firms take very different technological paths to reach a common performance level. As such, the races cannot be interpreted as a free-riding situation where one firm expands resources in advancing the state of technology and the others follow closely behind. Such spillover interpretations are suspect when products are in the region of high complexity, of high risk in succeeding, and different firms typically adopt different procedural and architectural approaches.

In this chapter we present a descriptive analysis of the evolution of technology with selected case studies to recount a story of technology evolution in a given industry. The logic underlying this evolution holds in any industry in which two broad sets of conditions are satisfied.

First, it pays for a firm to have a technological lead over its rival; it also boosts its market image and enhances its reputational capital.

Second, there are various levels of technological complexity among the products introduced by various firms. Technological complexity can be represented by a multi-criteria performance measure, that is, by a vector-valued distance measure. The collection of performance indicators and parameters, being connected with each other for individual firms, form an envelope that shapes a ‘technological frontier’. The technological frontier is in fact a reasonable indicator of the evolving state of knowledge (technical expertise) in the industry. At any point in time the industry technology frontier (ITF) indicates the degree of technical sophistication of the most advanced products carried by firms in that industry in view of comparable performance standards. Firm-level technology frontiers (FTF) are constructed analogously and indicate, at any point in time, the extent of technical sophistication achieved by the firm until that point in time. The evolution of firm- and industry-level frontiers is highly interactive. Groups of firm frontiers are seen to co-evolve in a manner that suggests that the respective firms are racing to catch up with, and get ahead of each other (Gottinger, 2006).

For illustrative purposes we emphasize three cases of knowledge-based industries for which we proceed to construct statistical indicators reflecting racing patterns in those industries: semiconductors/computers and telecommunications equipment (ICT), biotechnology, pharmaceutical and medical device industries. These industries are the major components of a knowledge-based network economy. Statistical indicators reflecting technology racing in those industries provide intrinsic information on knowledge leadership positions, competitive advantage and the level of welfare and wealth creation in the economies involved.

A data set could focus on a given set of products (systems) by major European, American and Asian enterprises in those industries for a sufficiently representative period of market evolution. In principle, we can identify at least two races in progress in the industries throughout a given period of duration. One comprises the world frontier race in each of those industries, the other, for example, the European frontier race which would technically constitute a subfrontier to the worldwide race. The aggregate technology frontier of the firms in a particular race (that is, ITF) is constructed in a manner similar to the individual FTFs.

Essentially, the maximal envelope of the FTFs in a particular race constitute the ITF for that race. The ITF indicates, as a function of calendar time, the best achievable performance by any firm in the race at a given date.

A statistical profiling of technological evolution and innovation is achieved for several major knowledge-based industries as they relate to competitive racing and rivalry among leading firms. Among the performance criteria to be assessed are (1) frequency of frontier pushing, (2) technological domination period, (3) innovations vs imitations in the race, (4) innovation frequency when behind or ahead, (5) nature of jumps, leapfrogging or frontier-sticking, (6) inter-jump times and jump sizes, (7) race closeness measures, (8) inter-frontier distance, (9) market leading through ‘market-making’ innovations and (10) leadership in ‘innovation markets’.

A race may or may not have different firms in the leadership position at different times. It may be a tighter race at some times than at others, and in general may exhibit a variety of forms of interesting behaviour. While interpretation and analysis of racing behaviour is left to subsequent chapters, it is appropriate to ask at this juncture why the firms should be racing at all. As access to superior technology expands the scope of opportunities available to the firms the technology can be applied in a range of markets. However, leading-edge technology is acquired at a cost. It seems unlikely that all the firms would find it profitable to compete to be at the leading edge all the time. Also, not every firm has access to equal capabilities in leveraging a given level of technological resources. Firms may, for example, be expected to differ in their access to complementary assets that allows them to appropriately reap the benefits from their innovation. It is reasonable to assume that whatever the level of competence of a firm in exploiting its resources it will be better off the more advanced the technology.

Based on this procedure an analysis will show how dynamic competition evolved in the past.

Chapter 3 entails a novel and unique statistical profiling of industry racing behaviour for selected high-technology industry cases.

The results yield valuable, policy-relevant information on the level of technological frontiers among local, regional and national enterprises, in leading-edge, high-growth, structurally dynamic and increasing returns industries in view of major competitors on the world frontier.

Unlike other (statistical) indicators (such as patent statistics) referring to the degree of competitiveness among industries, regions and countries concerned, the proposed measures cover behavioural, dynamic movements in respective industries, and are therefore able to lend intrinsic predictive value to crucial economic variables relating to economic growth and wealth creation.

The results are likely to provide strategic support for industrial and technology policy in a regional or national context and enable policymakers to identify strengths and weaknesses of relevant players and their environments in those markets.

The statistical indicators derived can be adapted and extended to other high-growth and fast-developing industries.

After presenting evidence of racing behaviour for some particular cases in Section 1.2, we generalize as to why multiple races might occur within an industry, and examine how lessons from these analyses influence and shape frameworks of technological evolution. Further on, in Section 1.3, we discuss frontiers and clarify why they are useful indicators of the evolving state of firm and industry knowledge. The basic pattern that emerges from the firm-level technology frontiers is indicative of racing behaviour. Section 1.4 presents a series of cases that highlight the manner in which strategic interactions between firms influence technological evolution. Section 1.5 shows the levels of conditions or constraints under which technological racing could evolve and is most likely to limit itself in the future.

1.2 Examples of technological races: old and new

In studying the evolution of high-technology industries, say over the last fifty years, one is amazed by observations on the intensity and universality of rivalry among competitors across a broad selection of industries. In many cases, developments of such industries were initiated and fostered by the interactive pattern of a continuous contest among market participants to get ahead of their rivals or not be left too far behind. We see these patterns emerging at various stages of market evolution and, at first sight, seemingly unrelated to market structures.

We identify those interactive patterns as technological races. First look at the minicomputer market in the early 1970s. At this time the Digital Equipment Corporation (DEC) was fighting its principal rival Data General (DG) which had been instantly successful because of its initial machine, the NOVA, and grew much faster than DEC did earlier in the market (Kidder, 1981). DEC’s reaction was the PDP 11, a carefully orchestrated response to the challenge of DG. From DG’s point of view, the most important 32-bit machinewas DEC’s VAX 11/780.Tracy Kidder’s account portrays Tom West as one of DG’s most talented engineers. He writes (Kidder, 1981, p. 29): ‘It has been painful for West and for a number of engineers working with him … to watch DEC’s VAX go to market, to hear it described as ‘‘breakthrough’’, and not have a brand-new machine of their own to show off’.

These reactions to rival product introductions form the basis for strategic interactions so crucial in determining the firm- and industry-level technology frontiers.

In the mainframe computer era, the Control Data Corporation (CDC) clearly regarded IBM as the enemy in the early stages of its history. CDC often had machines that were technologically superior to those that IBM was offering. For example, the high-end models of the CDC 6000 series, particularly the CDC 3600 and the CDC 6600, were technologically superior to even the highest end of the System 360 series that IBM introduced. Tom Watson, IBM’s chairman, was concerned by CDC’s activities, which became apparent in the following extract from an internal memo, dated August 1963, that emerged during an antitrust suit that the government filed against IBM (Lundstrom, 1988). ‘Last week CDC had a press conference during which they officially announced their 6600 system. I understand that in the laboratory developing this system there are only 34 people, including the janitor … Contrasting this modest effort with our vast development activities, I fail to understand why we have lost our industry leadership by letting someone else offer the world’s most powerful computer’. Watson wanted to have a new machine, and refused to be second best. IBM decided that a machine two-and-a-half times more powerful than CDC’s machine would be an appropriate target to aim for.

Watson attributed the success of IBM to IBM’s attitude to ‘running scared’ of the competition. We have other manifestations of intense rivalry and ‘neck-and-neck’ competition across industries, be it in advanced microprocessors between Intel and American Micro Devices (AMD) (see Markoff and Lohr, 2003), or specialty pharmaceuticals for medical care between Merck, Glaxo and Pfizer, and between biotech companies Amgen and Biogen. In the expanding market of software-related web services it is Microsoft against IBM, Sun Microsystems and Oracle (Lohr, 2003a,b), in consumer electronics and design it is Sony against Matsushita, Samsung or Sanyo (Belsen, 2003).

Leading in the technological race is often helped by network strategies based on network economies (Gottinger, 2003).

Flexibility and network strategy: the case of Motorola and Nokia, a case in standard-based competition

Let us take the case of Motorola and its Finnish competitor in the mobile communications markets, Nokia. During the 1980s and early 1990s, Motorola built dominance around the analogue AMPS mobile standard predominant in the United States. Motorola failed to recognize the emergence of digital standards, kept costs too high largely as a result of its dominance, and eventually lost its market position to Nokia, at the outset an obscure competitor. While there is no certainty in historical ‘what ifs’, had Motorola paid closer attention to developments worldwide in the mobile industry, not just the fragmented US markets, they might have headed the creation of substantial coalitions around emerging digital standards. In particular, they would have noticed the activities of Nokia and Ericsson, which took lead roles in attempting to drive change through coalitions, driven by a clear and consistently focused adaptable strategy (Roberts, 2004, p. 29). For a number of years, Motorola paid insufficient attention to the activities of these firms, as well as to the activities of foreign governments intent on helping to drive standards advantageous to their local firms.

Given that the mobile telecommunications industry is one of the most heavily characterized by network economics (e.g. supply and demand side economies of scale, standards, ‘winner takes most’), coalitions forming around competing standards should always recommend vigilant action by competitors. Had Motorola recognized the importance of these developments much earlier, it could have responded more effectively. It is important to recognize that this framework does not suggest that network-based strategies could only be answered by network-based strategies. Radical innovations could offset and leapfrog dominating network strength in network markets (Chapter 6). Motorola in the early 1990s certainly had the resources and market power to develop its own digital offerings in-house to answer Nokia’s threat. The important point of characterizing network strategies is to identify when a firm requires a competitive response, whether internally or externally focused, to other firms’ network strategies, as well as how best to approach building and executing a response. Had Motorola entered the digital arena much earlier, its most effective strategy would likely have been a network-based strategy. No major mobile communications standards have prevailed in any major world markets without a coalition of varied interests. Even in cases where government regulators mandated a standard, this occurred as a result of the actions of multiple interests. The Japanese and Korean governments’ mandate of specific national standards for second-generation wireless (2G wireless) occurred as a result of the interests of national firms, but in each case, multiple firms advocated for the standard. Certainly, dominant firms such as NTT in Japan exerted preponderant influence. As the wireless industry has grown globally, the creation of third-generation wireless standards (3G) has compelled all primary interest blocks – Japan, United States and Europe – to reach a global compromise.

Conversely, Nokia’s approach to the global marketplace focused the firm outside its boundaries. While Nokia maintained internal control over most product development efforts, it spent considerable resources discovering customer demands through extensive direct contact with consumers. The firm also supported innovation related to its product lines through numerous coalitions and alliances with smaller firms. This collaborative approach to ‘upstream’ innovation included such activities as standards development and innovation on technology inputs to the company’s products (e.g. improved semiconductors). Even internally, Nokia organized itself as a network of many small research teams located worldwide. Part of the motivation for this arrangement was to encourage true globalization of the Finnish firm. Finland’s isolation restricted its contact with foreign markets, and therefore foreign consumers, research organizations and firms. Management made a conscious decision to globally disperse its research teams, as well as to break them into small, flexible groups.

Nokia’s network strategy has provided it with a portfolio of strategic options in th...

Table of contents

• Cover
• Halftitle
• Title
• Copyright
• Contents
• Foreword
• Preface
• 1. Taxonomy of technological racing
• 2. Modelling stochastic innovation races
• 3. Statistical indicators of technological hypercompetition
• 4. Technological racing and competitive structures
• 5. Technological racing in asymmetric industry settings
• 6. Stochastic racing and competition in network markets
• 7. High-speed technology competition
• 8. Dynamic rivalry in technology platforms
• 9. Technological competition with product differentiation
• 10. Competition in increasing returns and network industries
• 11. Strategy and alliance formation
• 12. Aggregate technological racing: Economic growth, catching up, falling behind and getting ahead
• Index