Innovation as a Strategy in Network Markets Research Paper

This sample Innovation as a Strategy in Network Markets Research Paper is published for educational and informational purposes only. Free research papers are not written by our writers, they are contributed by users, so we are not responsible for the content of this free sample paper. If you want to buy a high quality research paper on any topic at affordable price please use custom research paper writing services.

Network markets are everywhere in the 21st century, a prevalent reminder that we are in an information age brought about by the information revolution. Prior generations of firms had to learn how to compete in markets brought about by the industrial revolution. In this century, many more will have to learn how to compete in a network market. This research-paper attempts to summarize basic concepts relevant to competition in these markets. What do we mean by a “network market” and a “network effect,” and how can firms compete through “innovation” in these markets? How does the type of innovation matter? How does the firm’s position—incumbent or challenger—matter? What are the relevant issues? Although these concepts and issues are not new, much of our thinking about them is.

The dominant characteristic of network markets is that the value of the product increases as the number of adopters increases. The marginal increase in value that these adopters attain when one more person joins the network is called a network effect. In short, the size of the network (installed base) creates a benefit, which is independent of any product features, quality, or even the image of the product—and this changes the nature of competition (Farrell & Saloner, 1985, 1986; Katz & Shapiro, 1985, 1986, 1992).

A fax machine, for example, is useless if it is the only one in existence, regardless of any “exceptional” features. Its value increases as the number of fax machines with which it can communicate increases. Therefore, we say a compatible set of fax machines form a communications network in which network effects are direct. However, indirect network effects can also arise when different components—such as hardware and software—work together in a system, and the value of one increases as the installed base of the other increases. A video game console, for example, becomes more valuable the larger the installed base of games it can play. An audio playback device such as the iPod becomes more valuable as the library of music it can play grows larger (Clemens & Ohashi, 2005; Gallagher & Park, 2002).

Network markets are not new. FM and AM broadcasting systems exhibit network effects, as do electric transmission systems and even the relatively ancient Pony Express. However, network markets are arguably much more prevalent this century, given the central role of new communication and information processing technologies in our lives; many of us have had to choose among mobile phone operators, for example (Birke & Swann, 2005). When one also considers that the firm, as we know it, has only existed for a century or two, it becomes clear that we have a lot to learn about competition in these markets (Chandler, 1977).

This research-paper addresses this emerging area of knowledge and focuses on technological innovation as a strategy in these markets, particularly product and systems innovation. Whether innovation in a network market is likely to capture share and profits clearly depends on several factors. Prominent among these are (a) market structure—whether the market remains competitive or is dominated by a monopolist; (b) the position of the innovator—peer, challenger, or monopolist; and (c) the type of innovation—the extent of compatibility and improvement it provides relative to competitors’ products. Radical innovation provides large improvements and incremental innovation, small ones.

As we review what we do and do not know about competing through innovation in network markets, we find challengers may be better off adopting more risk, not less. Both incompatible and radical innovation can offer higher expected returns than compatible and incremental innovation, respectively. Moreover, “traditional” strategies such as competing through product benefits and differentiation remain highly relevant—even though scholars initially advised challengers to concede network markets that had “tipped” to a dominant firm.

Such prescriptions underestimate the powerful role of innovation as a strategy and the competitive process by which new technology periodically replaces the old. Fax machines, for example, are now largely replaced by “scan and send” technologies in computer systems. Network effects clearly raise the bar for challengers, and they may confound some of what we know about competition, but they do not negate the entire body of knowledge that management scholars and economists have painstakingly accumulated.

The first section of this research-paper proceeds to summarize what we know about competition in network markets. As we shall see, much of the management research in this area has focused on network markets. The second section addresses the roles that firm position and type of innovation play in markets that have tipped to a dominant firm. The third section then analyzes competition in this postemergent phase: how can a challenger compete after a monopolist has won a standards war and captured a “winner-take-all” position? Finally, we distinguish among different types of network markets, by pointing out that the type of innovation a challenger should use to compete is a function of market and technological characteristics. Some characteristics render some types of innovation far more likely to capture share from a monopolist than others.

Network Markets

Each scholar has his or her favorite source of knowledge regarding some phenomenon. This case is no different. There appear to be four major research streams that introduced ideas related to network markets, what they are, and how they differ from the traditional markets that scholars had previously focused on. These sources consist of (a) business histories such as the VHS and Beta wars documented by Rosenbloom and Cusumano (1987);   (b) Arthur’s (1989) analysis of increasing returns; (c) David’s (1985) description of how expectations and compatibility issues lead to path dependence; and (d) the formal economic models developed by Farrell and Saloner (1985, 1986) and Katz and Shapiro (1985, 1986), which described network effects and how they affect competition and social welfare. This latter body of work is broadly referred to as network externalities theory.

Winner Takes All

To date, most studies of network markets have focused on the dynamics of competition in emerging markets. Proponents of network externalities theory assert that incompatible technologies compete intensely in emerging markets, but when consumers expect the installed base of one technology to become larger than any other, they adopt that technology en masse, abandoning any other. That point where consumers expect a technology to win is called a tipping point because the market tips to adopt that technology to the exclusion of any other.

One of the most notable aspects of competition in these markets is that it becomes a do or die proposition. Competition is particularly intense because just one technology remains standing. If one firm has proprietary access to that technology, the end result is one monopoly and monopoly profits. The other competitors are vanquished and retain virtually no market share. Moreover, such a monopolistic position appears quite sustainable, since network effects deter others from competition. As a result, these monopolists have been considered invulnerable.1

Thus, the term winner-takes-all characterizes this type of competition. The winning firm, that which owns the most popular technology, takes “all” the profits. Fringe competitors and new entrants bite the dust. Microsoft’s monopoly share of the desktop (notebook) operating system market is a popular example of such a winner-take-all position.

Expectations and Compatibility

Key to this dynamic are the roles of expectations and compatibility. Expectations are self-fulfilling in network markets; they create a positive feedback loop. When consumers expect a product will attract the most consumers, they will buy that product, which causes the market to tip and that product to have the largest installed base. In competitions between systems that exhibit indirect network effects, consumer expectations about the availability, price, and quality of some components can be determinative when other components must be bought first. If consumers do not expect software components to be available, for example, they will not buy hardware components and, hence, the overall system. Expectations regarding these components determine which technological system wins the market.

As a result, firms have strong incentives to build expectations about their own products and tear down expectations about rival products. Some of the legitimate ways firms build expectations are through sources of competitive advantage such as established reputations, well-known brand names, and visible access to capital. Less legitimate tactics such as preemptive product announcements and predatory pricing have received the attention of antitrust agencies such as the Department of Justice in the United States and its counterpart in the European Union. Apparently, the promise of sustainable monopoly profits promotes hypercompetition that walks a fine line between business practices that are considered predatory and those that are not (Sheremata, 1998).

Firms without the previously mentioned sources of competitive advantage are more likely to pursue an open systems strategy in which technological specifications are made available to encourage compatible product development and larger networks. These firms are more likely to prefer to compete through compatible products. They compete within—rather than between—standards, those technological specifications that determine the extent to which products can work together. In contrast, firms that do have established reputations, large sources of capital, and other advantages are more likely to compete through incompatible products—between standards (Besen & Farrell, 1994).

Compatibility has been broadly defined as the ability of a product to work well with another (Farrell, 1989). More specifically, communications networks are incompatible when a subscriber of one network cannot communicate with those on another network; “hardware/software” networks are incompatible when components of one system do not work with components of another system. EMI produces CDs, for example, that are “copy controlled” so they cannot be played on an iPod through iTunes. These CDs are components of audio systems that are incompatible with the iTunes system.

David (1985) explained the critical roles of expectations and compatibility in his history of the QWERTY design, which still dominates (as you can see from the top row of letters on your keyboard). He argued that this dominance is a historical accident because this sequence was chosen to prevent mechanical keys from jamming in a typewriter, which is no longer a problem. This design became locked in, meaning users became highly resistant to alternatives because it was the first to be widely adopted. Subsequent designs were incompatible and created switching costs for (a) typists who had learned the QWERTY design and (b) institutions that had trained them to type at record speeds. If these users and institutions switched to new designs, their prior learning and skill base would become worthless.

The QWERTY story and Rosenbloom and Cusumano’s (1987) history of the home video wars (Beta vs. VHS) were among the first to capture the essence of compatibility issues in hardware/software network markets, where systems compete for market share. In the QWERTY case, the keyboard is the hardware; typist and training skills can be considered software. In the home video case, the recorder/ playback device (the VCR) was the hardware; the videotape was the software. The primary issue in these markets was whether software components designed to work in one system would work in another.

Typists trained to speed-type on QWERTY keyboards would find their skills devalued (and perhaps useless) if another design replaced QWERTY—even if it were technologically superior and allowed a new generation to type twice as fast. When VHS won its standards war—when a majority of consumers expected VHS would become the most popular format, causing the market to tip to the

VHS standard—consumers abandoned their Beta tapes and VCRs in droves, so they could access the variety of VHS tapes that quickly flooded the market. Some became dual households for a while, but eventually folded.

Hence, we come across a phenomenon that plays a big part in adoption decisions: the fear of being stranded. If consumers adopt the losing technology, their prior investments in learning, skills, hardware, and software libraries lose substantial value. They will not be able to access future improvements associated with the winning technology.

Coordination in Systems Competition

A system consists of two or more components and an interface that allows them to work together. Hence, competition among systems brings up the issue of coordination (Katz & Shapiro, 1994). Consumers coordinate their choice of hardware based on their expectations of software availability (or other components). At the same time, firms must ensure sufficient “software” components are available for the “hardware” they produce. They can accomplish this by (a) owning all the relevant components, (b) establishing long-term contracts with other component owners, or (c) participating in formal standard setting bodies (such as the American National Standards Institute and the International Standards Organization).

Consider the generation of camcorder consumers who have been stranded with 8-mm videotapes of priceless family moments and (virtually) no devices with which to play them back. Markets have moved on to produce and adopt other camcorder formats, which (arguably) provide better performance (in some dimension) or are more cost-effective for the majority. Although many 8-mm consumers also bought new and incompatible generations of camcorders, we suspect they did not foresee that newer formats would eventually eliminate support for prior standards, leaving them without devices to play back birthday parties and weddings. But so it has been, since the dark ages of the 33-inch LP (long playing vinyl record) and even before that. When consumers adopt new and differing technologies so quickly that stranding is excessive and (overall) value is destroyed, we say these markets exhibit excess momentum (Farrell & Saloner, 1986).

In an attempt to solve this problem, consumers may delay their choice of technology until they can be relatively sure they will not be stranded. In some cases—where technological change moves quickly and a variety of products are available based on competing standards—consumers can delay purchases indefinitely. As a result, these markets display excess inertia. They either fail completely because there is no foreseeable market large enough to sustain these products, or they fail because no single technology (firm) can attain a share that is large enough to generate profits. The competition surrounding high-definition TV has demonstrated many aspects of excess inertia, for example. Economists claim these consumers would be better off if they could coordinate their decisions, because that would allow a large enough market to exist. More new technologies would be brought to market and fewer consumers would be stranded.

Now consider Apple’s iPod as a hardware component in a competition among portable audio playback systems. This hardware/software system has become a dominant standard for consumer playback of audio—on the go—and seeks to include more functionality by adding interfaces to a broader set of components. One of those components is Microsoft Outlook, which maintains contact data such as addresses and phone numbers. Apple provides instructions on how to import contacts from Outlook, and the result (when it works) is truly more functionality and ease of use. However, slight shifts in interfaces between components can create incompatibilities and what is, theoretically, a simple import can become a very complicated exercise. In such a case, compatibility might even be blocked by the owner of one component in order to prevent the other from gaining share.2 It will be interesting to observe the extent of compatibility and coordination Apple Inc. can achieve with third parties, as it attempts to provide more and more functionality in products such as the iPhone and “Apple TV.”

Competition In Network Markets

Economists have produced the lion’s share of what we know about network markets. The pioneering work of Katz and Shapiro (1985, 1986) and Farrell and Saloner (1985, 1986) has been particularly productive. Overall, however, economic research has focused on market failures. Social (producer and consumer) welfare is reduced when networks are underutilized, meaning users do not join a network that would benefit them or they cause excessive stranding by joining networks too quickly. Economists see this as a coordination problem that can sometimes be avoided by integrating components within one firm or having large buyers sponsor networks. There are also pricing, contract, advertising, and reputation mechanisms that firms can use to integrate component owners and convince consumers that a network will grow.

Economists are also concerned about the longevity of monopolistic power derived from network effects, since it appears immune to competitive attacks (Microsoft’s hold on desktop operating systems is a good example). They are usually concerned about the threat (to social welfare) of monopolistic prices and hefty margins. However, economists disagree about the ultimate effects of monopolies. Schumpeter (1950) argued that monopoly profits were important sources of funds for large-scale innovations, while management scholars argue that monopoly profits increase wealth, employment, and (sometimes) technological innovation.

What management scholars share with economists is an interest in (a) how challengers can compete against dominant incumbents and (b) innovation. Many economists have concluded that the sheer longevity of a monopoly protected by network effects can reduce the speed of innovation, while an important segment of the management community focuses on how firms can successfully compete through innovation. Despite this commonality, few in the management community have studied competition in network markets.

The remainder of this section summarizes what we know about strategic management in emerging network markets, after which we will return to the following issues: (a) How can a challenger compete against a dominant incumbent in a network market, and (b) how can firms compete through innovation in these markets? As we shall see, these issues require a more in-depth analysis of the type of innovation through which a challenger can compete as well as the market itself. Apparently, more comprehensive analyses of innovation types and market characteristics bring us, full circle, back to traditional competitive strategies such as product differentiation. The foundation of knowledge upon which Porter (1980, 2001) built his frameworks remains applicable to network markets. However, traditional models of strategy remain relevant only after one broadens the underlying analyses to include “the effects” of network effects and additional characteristics of supply and demand.

Tactics in Emerging Markets

Strategic management scholars are not interested in determinative theories of monopoly power. Because they focus on how firms can compete, scenarios where profits are locked up for generations hold less interest for them than those where competition can lead to market share and profits. As a result, most management studies to date have focused on how firms should compete in emerging network markets—those that have not yet tipped—before the winner takes all.

Both economists and management scholars have identified tactics that firms can use to attract consumers to networks in emerging markets. Among others, these include

(a) making credible and binding pricing commitments;

(b) opening the market to software suppliers to ensure users of an alternate “second source” supply; (c) renting rather than selling hardware so firms incur risk rather than consumers; (d) integrating, or forming an alliance, to signal commitment to sell both hardware and software; (e) penetration pricing, providing steep initial discounts; (f) making sunk investments to show commitment to software supply while signaling expectations of heavy demand; and (g) holding important firm assets such as reputation hostage (Katz & Shapiro, 1994). All of these tactics can affect expectations and, therefore, influence the market to tip toward a firm’s product.

Strategies in Emerging Markets

Besen and Farrell (1994) are among the few who have tried to take a comprehensive look at competition in these markets from a strategic management perspective. Given a competition between two firms in an emerging network market, they focus on a basic strategic choice: Should a firm prefer to compete within or between standards? That is, should a firm make its products compatible with those of its rival, competing within a standard, or should it make them incompatible, competing between standards?

The key question for firms is whether competing for, or within, the market is more profitable. When firms are symmetrically positioned with respect to resources, reputation, and other sources of advantage, Besen and Farrell (1994) assert that a firm’s return will depend on two variables: (a) the degree of skew in expected returns and (b) the sharpness of available tactics. The more skewed returns are, the harder firms will fight, and the sharper the available tactics, the more fighting will dissipate profits.

From these two variables, Besen and Farrell (1994) formulate three scenarios: (a) “Tweedledum and Tweedledee,” in which both firms prefer to compete to set the standard and so have a standards battle; (b) “Battle of the Sexes,” in which each prefers its own technology as the standard, but also prefers compatibility with its rival’s standard to going it alone—compatibility is important and both prefer to compete within a standard; and (c) “Pesky Little Brother,” in which one firm prefers to maintain its technology as a proprietary standard, but the other wishes to join its rival’s network. They provide more detail on the competitive dynamics that ensue and an entertaining account of each scenario.

Open Standards?

Finally, scholars such as Garud and Kumaraswamy (1993) have also looked at the importance of compatibility in competition, by focusing on the role of open standards. Sun Microsystems and IBM’s Personal Computer both illustrate the dynamics of “open” competition. Both clearly obtained advantage through open systems. Other firms could make their products compatible because Sun and IBM made their interface specifications widely available. However, IBM’s advantage was clearly not sustainable; imitation became a widespread problem and its Personal Computer rapidly lost market share after a few very successful years. In contrast, Sun was able to retain its advantage for a longer period by retaining a greater degree of intellectual property protection and rapidly upgrading its products. Again, however, studies of open systems have focused on competition in emerging markets.

Critics and Future Developments

Not all are converts to the basic tenets of network externalities theory. Liebowitz and Margolis (1994, 1999) have been vocal critics, pointing out what they consider fatal flaws in this body of theory. They and others suggest that more is here than meets the eye. Perhaps failing technologies only appear inferior, for example. They also point out that additions to networks cease to produce network effects at some threshold in some markets. Moreover, heterogeneous preferences among consumers appear to negate some of the tenets of network externalities theory. However, what is a gap or flaw to one scholar is an opportunity to another—an opportunity to extend theory. The following section addresses some of these “flaws” in the context of postemergent network markets. We return to the issue of how challengers can compete through innovation in markets that have already tipped.

Competing Through Innovation

In the last decade of the last century, the prevailing thought was that all was won or lost after a network market tipped to a dominant firm. Challengers—those fringe competitors with tiny shares and new entrants—could not compete. The idea that innovation could overturn a monolith such as Microsoft, in the Schumpeterian fashion, was not really considered. This is where 21st-century scholars have their work cut out for them: How can firms compete through innovation in network markets after a winning firm has become entrenched and network effects amplify barriers to entry?

Challengers Versus Incumbents

Initial formulations of network markets have revolved around the idea that powerful incumbents with entrenched monopoly power result from tipping. This winner-takes-all mentality has become so firmly established that the dominant prescription for firms that lose standards battles is to exit the market (Arthur, 1989, 1996). This presumption that firms cannot successfully challenge the winner—combined with the gaps in current theory that critics point out—provides tremendous opportunities to advance our knowledge about these markets.

This problem falls squarely in the domain of strategic management, whose mission is to help firms compete no matter how dire the straights. Moreover, the idea that incumbents cannot be overthrown—through innovation—is one that fundamental research in management and economics contradicts. Tushman and Anderson (1986) clearly demonstrated the discontinuous and cyclic pattern of competition, in which radical innovation periodically overthrows prior technological regimes. Such analyses fall squarely in line with Schumpeter’s (1950) theories about “the process of creative destruction” (p. 81), by which he meant extensive technological improvements periodically lead to new social and economic orders.

More recently, economic models indicate that the question of whether a challenger or incumbent monopolist has sufficient incentive to compete through innovation depends on (a) the degree to which the innovation destroys the monopolist’s market power and (b) the extent of technological uncertainty. Economists find that innovation is a key strategy that incumbents can use to hold onto monopoly power, but challengers can also use this strategy to wrest monopoly power from them. Moreover, the type of innovation matters—radical innovation can favor challengers, as we shall see. These models are consistent with management research that finds different types of innovation lead to different competitive outcomes, which also depend on whether the firm is a challenger or incumbent (Henderson & Clark, 1990).

Although these studies have focused on nonnetwork markets, we need only look at what differentiates a network market and factor that into their analyses. A network market is not a completely different animal from a non-network market. It merely consists of additional characteristics—stripes, if you will. The distinguishing feature of a network market is the presence of network effects, which means increases in network size confer a benefit to those in the network. However, network benefits are not the only benefits that adopters incur. As Katz and Shapiro (1992) demonstrated, product and network benefits coexist side by side and appear largely independent. If product benefits are large enough, and consumers expect the new network can attain some minimally sufficient size, product benefits can clearly substitute for network benefits. That is, large product benefits can compensate consumers for the network benefits forgone to join the smaller (challenger) network instead of the dominant one.

Some of the more visible network markets are those in which the size of the network benefit has clearly overwhelmed the size of product benefits that challengers have tried to compete with (the market for desktop operating systems, for example, which Microsoft has dominated). However, history indicates that—even in these markets— innovation that provides an overwhelming advantage over existing technology can topple incumbent dominance and establish a new network. Video communications, for ex-ample, can topple the dominance of telecommunications networks, and so forth. Product benefits, and strategies such as product differentiation, are still relevant in network markets (Greenstein & Mazzeo, 2006). They merely have a higher threshold to overcome; large rather than incremental improvements must often be provided.

Types of Innovation

Challengers in markets that have tipped must make two strategic choices regarding innovation type. They must decide whether to compete through compatible or incompatible innovation. However, they must also choose the extent of improvement they will provide, whether they will compete through radical or incremental innovation (David & Greenstein, 1990; Sheremata, 2004).

Recall that radical innovation provides large improvements in function or performance, relative to cost, whereas incremental innovation provides minor improvements. Innovation is radical to the extent that it embodies new knowledge. An electric car is a radical innovation, for example, whereas new dashboard features are usually incremental. Although most management research has been vague about the extent to which innovation is radical or incremental, a few—such as Henderson (1993) and Christensen (1997)— have explicitly studied how radical innovation affects competitive outcomes. Gilbert and Newbery (1982) initially found that incumbents with monopoly power should prefer to invest in innovation when entry barriers are low, to preempt entry. Consistently, Christensen has shown that powerful incumbents often engage in “disruptive” innovation. However, Reinganum (1983) found that incumbent monopolists have less incentive to innovate than challengers when technological uncertainty is high—as it is in radical innovation.

In spite of this progress, the mind-set of addressing incremental, rather than radical, innovation has been prominent in the 20th century. Rosenberg (1982), among other business historians, economists, and management scholars, argued that the majority of technological progress has been achieved through small incremental innovations that build upon each other. Usher’s (1954) history provides examples from turbine engines to precision timepieces that have been developed this way. While it is true that the vast majority of innovations are incremental, such a focus ignores the impact of those rare, but truly new, innovations that change our social landscape. As Schumpeter (1950) argued, radical innovation is what truly propels profits and technological progress. Although such innovations are rare, their impact on competition and social welfare is so great that they too deserve study. Innovation-related theories that claim to be “one size fits all” typically are not (Abernathy & Utterback, 1978).

Challenging An Incumbent Monopolist

Our knowledge of how to compete in emerging network markets has clearly progressed, as described earlier. However, management scholars have paid far less attention to competition in network markets after they have tipped to a dominant firm.3 These markets have a very high barrier to entry, since network effects amplify traditional barriers such as economies of scale and capital requirements.

Innovation is a way of competing in these markets that appears to have been underestimated. New entrants and fringe competitors can topple the incumbent to capture significant market share, but the way they do this—the extent to which innovation is not only compatible, but also radical—matters. Moreover, we need a more comprehensive review of market characteristics—consumer value and production functions—to understand how challengers can compete. The properties of network markets are simply more complex than initially envisioned. More in-depth analyses can bridge the gap between critiques of network externalities theory and its potential to help firms compete.

Consistent with these critiques—which point to omitted variables—firms need to analyze additional characteristics of markets and technologies when formulating a strategy to “take back” a network market. In combination, these characteristics render some types of innovation far more likely to succeed than others. Such analysis can help challengers determine whether their product should be compatible with the dominant firm’s product and the extent of improvement they should provide—how much additional product benefit. In short, should they compete through innovation that is incompatible or compatible, and radical or incremental?4 Which combination is most likely to capture significant market share and returns?5

Competing Through Incompatible Innovation?

As described earlier, scholars have addressed the issue of whether firms should compete through compatible products in emerging network markets. This issue is also central in the context of a tipped market, but the competitive dynamics differ.

Intellectual Property Protection

First, a challenger must determine whether competing through a compatible product is even feasible. Assumptions regarding the extent to which firms can produce compatible products are critical, but often not addressed. Imitation is deterred by informal means such as technological complexity and lead times, as well as formal mechanisms such as copyrights and patents. Although the latter are not as costly to work around as many presume, informal mechanisms can be quite costly (Lemley & Shapiro, 2005; Levin, Klevorick, Nelson, & Winter, 1987; Varian, 2005).

When intellectual property protection precludes perfect compatibility, the challenger then needs to decide the extent of incompatibility it will provide. It must weigh the benefits of greater compatibility against the costs of working around existing protection. However, it must also evaluate the benefits of incompatibility. Market characteristics—such as heterogeneous preferences and low thresholds for network effects—can enable the coexistence of multiple networks, which creates opportunities for incompatible innovations. Incompatible innovation may then have greater expected returns than compatible innovation.

Hence, firms need to analyze the structure of demand to determine whether preferences differ in the market. They also need to evaluate whether the network effect—the benefit conferred by adding one more user to the network— wanes or ceases to exist at some threshold. If preferences differ and network effects become insignificant at some relatively small network size, then multiple networks can coexist and incompatibility may confer more benefits than initially apparent.

Heterogeneous Preferences

Even if perfect compatibility is an option, challengers may profit more from a strategy of incompatible innovation if consumers value heterogeneous aspects of products—simply because perfect compatibility precludes heterogeneity. Joe Farrell (1989) gave a wonderful example of this when he described how horses tethered together cannot coordinate themselves to satisfy differing preferences. If one horse craves a meal in the shade and the other a patch of clover some distance away, one must sacrifice its preference for the other. Compatibility precludes the satisfaction of mutually exclusive preferences.

Because differing preferences can be latent, firms must carefully analyze demand to determine whether dimensions of value upon which buyers differ can be found. Economists have long held that competition should be more profitable when firms differentiate products, that is when they produce products that satisfy strongly held tastes for a variety of product characteristics (Scherer, 1992). Differentiation has also been a central strategy in the field of management (Porter, 1980). However, practitioners and scholars have assumed that network effects render differentiation strategies obsolete—that product benefits do not matter when network benefits are in play. They do matter.

Differentiation is a strategy that can work in network markets, if it reflects market demand. Incompatibility can be more profitable—given varied preferences in the market—than a head-on competition to meet the same preferences, which can dissipate profits. That said, the benefit provided by meeting demand for variety must exceed the network benefit provided by the dominant firm. Variety must be valued more than the forgone network benefit. We see this periodically, for example, when consumers value features of Apple’s computer systems more than the network benefits conferred by the dominant Wintel (Windows and Intel) standard.

Network Thresholds

Some mathematical function reflects the relationship between network size and network effects, how much benefit all users (members) obtain as each new user is added to a network. Is that function linear—meaning the addition of each new “user” increases the value of belonging to the network just as much as each prior addition—or, at some point, do increases in the size of the network (installed base) have weaker network effects? At some point, do additions to the network cease to benefit users? Do network effects wane such that they become inframarginal and cease to matter? If so, what may appear to be one large network market may actually be able to accommodate incompatible products and the coexistence of several smaller networks.

Consider the benefit of adding one more person to a party in a small house. In general, adding the fifth or sixth person should liven things up, make for interesting conversation, and so forth. Each additional person should provide benefits to the people already invited, until the food runs out, the house is too small, and you cannot really find the person you want to talk to. At some point, depending on individual preferences, adding one more person subtracts value. A positive network effect no longer exists at that point.

Increases to the size of the network simply fail to add value at some particular threshold, which means multiple networks (parties) can coexist. We see this all the time—in markets for incompatible video game consoles, for example. Yet, Microsoft’s sustainable dominance of the market for desktop operating systems has led some to gloss over this aspect of network markets. It is difficult for multiple operating systems to coexist because the threshold at which network effects wane is quite high. Moreover, preferences for desktop operating system functions do not vary substantially; they have been relatively homogeneous. Even here, however, niches can be found. As West and Dedrick (2006) describe, Linux is able to coexist with Windows in segments where preferences differ and small networks provide value.

Competing Through Radical Innovation?

If a challenger does determine that competing through an incompatible product is unavoidable or beneficial, it must then determine how much improvement (product benefit) it needs to provide consumers to compensate them for network benefits forgone. The greater the network size the dominant firm provides, the greater the product benefit the challenger must provide. Moreover, characteristics such as switching costs, R&D cost structures, and technological uncertainty impose additional risks and costs on investors and consumers. Therefore, the challenger must decide how radical its product needs to be to compensate for all of these costs. Ultimately, the degree of product benefit a challenger provides determines whether it can surmount the “net” entry barrier—the traditional barriers to entry amplified by network effects. In sum, these characteristics affect expected returns from radical and incremental innovation, rendering one of these strategies more preferable than the other.

Switching Costs

Consumers will simply not switch to a new and incompatible technology unless it offers significant improvements in performance (Shapiro & Varian, 1999). Conversely, they will switch if the challenger provides sufficient benefits. Switching costs are those costs consumers perceive they will incur if they replace one product with another. They include psychological costs—such as a fear of incompatibility—the cost of learning new skills to replace those rendered obsolete, as well as the cost of replacing physical components. They occur in both network and nonnetwork markets.

Switching costs and the degree of incompatibility need not be related. Many people prefer brand-name pharmaceuticals even though the compositions of generic drugs are virtually identical. Moreover, switching costs depend on the specific market for which a firm is competing. Professional programmers, for example, incur fewer switching (learning) costs than nonprofessionals when upgrading software products.

Whatever the source, if the market a challenger targets has switching costs, it must provide product benefits that compensate consumers for those costs as well as forgone network benefits. When switching costs are high, radical innovation should be the preferred strategy, since it is the only type of innovation capable of providing a large enough product benefit. Incremental innovation simply provides too little improvement to convince buyers to incur switching costs and give up the greater network benefit of the larger network. Radical innovation should be more profitable than incremental innovation; expected returns should be higher.

R&D Cost Structures

Certain cost structures have similar effects, and should lead challengers to prefer radical over incremental innovation. When R&D costs are fixed and production economies of scale exist, investors incur greater risk and uncertainty as to whether they can recover their investments. Such a cost structure amplifies the barrier to entry that already exists in a tipped network market.

Challengers with high R&D fixed costs require high revenues to recoup their investment. However, incremental innovation cannot command the prices (or market share) necessary to recoup large up-front investments. Nor can incremental improvements convince consumers to forgo network benefits from a larger network. Only large improvements can compensate consumers for network benefits forgone and investors for a high degree of risk. Therefore, only radical innovation is consistent with such a cost structure; only large improvements have any chance of rendering a positive return.

Radical innovation already carries a high degree of risk, and this type of cost structure adds to that. Such innovations have a low probability of success. However, in this context incremental innovations have no chance of success. Moreover, firms often fund dozens of projects, knowing that only one needs to succeed. These firms treat each project as an option, which they can cut short—not fully fund—when other projects indicate more promise. Alternatively, entrepreneurs can bet the farm on one “shot,” knowing radical innovation offers them a greater chance of success than incremental innovation.

Technological Uncertainty

Finally, the materials from which a product is developed create uncertainty regarding when it can be delivered to market. This irreducible component of technological uncertainty stems from the nature of the technology. Many of today’s products consist of information rather than physical components, which can clearly increase irreducible technological uncertainty.6 This, in turn, increases investors’ risk. Again, incremental innovation cannot provide sufficient

value to compensate investors for high levels of risk and consumers for network effects forgone. Again, radical innovation is more likely to be profitable than incremental innovation; expected returns should be higher.

Most of the variables just described are common in industrial organization (IO) economics, a field that examines characteristics of demand and supply to predict how different strategies (firm conduct) affect market structure (whether the market remains competitive or not), given firm positions (performance). Michael Porter (1981) successfully took the IO model of performance — conduct — structure and “flipped it around” to look at these variables from a firm’s perspective: structure — conduct — performance. How might a firm take the same understanding of the same variables to attain competitive advantage and higher than average profits? This research-paper suggests that a 21st-century analysis of network markets has much to obtain from taking a similar approach, from breaking down the elements of demand and supply into key variables that can be used to model “the effects” of network effects. It is time to develop further sophistication in our models of how firms can compete in these markets and how they can compete through innovation.


The field of strategic management is in its infancy, compared to others. It has only been a couple of decades since the field coalesced around concepts promoted by Porter (1980), Schendel and Hofer (1979), and Mintzberg (1977), among others. Porter’s work is particularly relevant to our topic because he built upon economic theory to produce a framework for strategy formulation that has proven remarkably durable. Some have suggested that network markets challenge that framework. Porter (2001) himself, however, demonstrated that his models apply to Internet and information-related markets. His frameworks clearly provide valuable insights to any industry.

That said, traditional strategic frameworks simply do not address the unique facets of competition in network markets. They do not address the central idiosyncrasies of such competition, and so fail to capture the essence of competing in these markets. Like the “dark side” of the Star Wars series, the “demand side” of competition—the demand-side economies of scale that characterize network markets—are unfamiliar to many and present unique challenges. Let us return to what we do and do not know about these markets.

We know some of the basic dynamics of competition in emerging markets, but far less about how to compete in markets that have already tipped. We know something about competing through compatible standards—and open systems—but far less about competing through incompatible and radical innovation, particularly in monopolized markets. Moreover, we have yet to move beyond simplistic archetypes—emerging or monopolized—to address the unique characteristics of a variety of network markets. Finally, we need to know more about how characteristics of demand and supply affect competition in these markets.

The good news is that these challenges do not exceed the capabilities of existing research methods and paradigms, particularly those introduced by the field of IO economics and leveraged by Porter (2001). Moreover, the study of innovation has tremendous potential to contribute further insight, since research that addresses the type and context of innovation is also in its infancy. Therein lies an exciting challenge for the 21st century: How can firms compete through innovation in network markets? Meeting this challenge has widespread implications for firms, their investors, and social welfare as a whole. This author is one who truly believes innovation raises all boats.


  1. Abernathy, W., & Utterback, J. (1978). Patterns of industrial innovation. Technology Review, 2, 40-47.
  2. Arthur, W. B. (1989). Competing technologies, increasing returns, and lock-in by historical events. Economic Journal, 99(394), 116-131.
  3. Arthur, W. B. (1996). Increasing returns and the new world of business. Harvard Business Review, 7(4), 100-109.
  4. Besen, S., & Farrell, J. (1994). Choosing how to compete: Strategies and tactics in standardization. Journal of Economic Perspectives, 8, 117-131.
  5. Birke, D., & Swann, G. M. P. (2005). Network effects and the choice of mobile phone operator. Journal of Evolutionary Economics, 16, 65-84.
  6. Chandler, A. D. (1977). The visible hand: The managerial revolution in American business. Cambridge, MA: Belknap Press.
  7. Christensen, C. M. (1997). The innovator’s dilemma: When new technologies cause great firms to fail. Cambridge, MA: Harvard Business School Press.
  8. Christensen, C. M., Verlinden, M., & Westerman, G. (2002). Disruption, disintegration and the dissipation of differentiability. Industrial and Corporate Change, 11, 955-993.
  9. Clemens, M. T., & Ohashi, H. (2005). Indirect network effects and the product cycle: Video games in the U.S., 1994-2002. Journal of Industrial Economics, 53(4), 515.
  10. Cusumano, M. A., Mylonadis, Y., & Rosenbloom, R. S. (1992). Strategic maneuvering and mass market dynamics: VHS over Beta. Business History Review, 66(10), 51-94.
  11. David, P. (1985). Clio and the economics of QWERTY. American Economic Review, 75, 332-337.
  12. David, P. A., & Greenstein, S. (1990). The economics of compatibility standards: An introduction to recent research. Economics of Innovation and New Technology, 1, 3-41.
  13. Farrell, J. (1989). Standardization and intellectual property. Juri-metrics Journal, 50, 35-50.
  14. Farrell, J., & Saloner, G. (1985). Standardization, compatibility, and innovation. Rand Journal of Economics, 16, 70-83.
  15. Farrell, J., & Saloner, G. (1986). Installed base and compatibility: Innovation, product preannouncments and predation. American Economic Review, 76, 940-955.
  16. Gallagher, S., & Park, S. H. (2002). Innovation and competition in standard-based industries: A historical analysis of the U.S. home video game market. IEEE Transactions on Engineering Management, 49(1), 67-82.
  17. Garud, R., & Kumaraswamy, A. (1993). Changing competitive dynamics in network industries: An exploration of Sun Microsystems’ open systems strategy. Strategic Management Journal, 14, 351-369.
  18. Gilbert, R. J., & Newbery, D. M. G. (1982). Preemptive patenting and the persistence of monopoly. American Economic Review, 72, 514-526.
  19. Greenstein, S., & Mazzeo, M. (2006). The role of differentiation strategy in local telecommunication entry and market evolution: 1999-2002. Journal of Industrial Economics, 54(3), 323-350.
  20. Henderson, R. (1993). Underinvestment and incompetence as responses to radical innovation: Evidence from the photo-lithographic alignment equipment industry. RAND Journal of Economics, 24(2), 248-270.
  21. Henderson, R., & Clark, K. B. (1990). Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Administrative Science Quarterly, 35, 9-30.
  22. Katz, M., & Shapiro, C. (1985). Network externalities, competition, and compatibility. American Economic Review, 75, 424-440.
  23. Katz, M., & Shapiro, C. (1986). Technology adoption in the presence of network externalities. Journal of Political Economy, 94, 822-841.
  24. Katz, M., & Shapiro, C. (1992). Product introduction with network externalities. Journal of Industrial Economics, 40, 55-84.
  25. Katz, M., & Shapiro, C. (1994). Systems competition and network effects. Journal of Economic Perspectives, 8(2), 93-115.
  26. Lee, E., Lee, J., & Lee, J. (2006). Reconsideration of the winner-take-all hypothesis: Complex networks and local bias. Management Science, 52(12), 1838-1848.
  27. Lemley, M. A., & Shapiro, C. (2005). Probabilistic patents. Journal of Economic Perspectives, 19(2), 75-98.
  28. Levin, R. C., Klevorick, A. K., Nelson, R. R., & Winter, S. G. (1987). Appropriating the returns from industrial research and development. Brookings Papers on Economic Activity, 3, 783-831.
  29. Liebowitz, S. J., & Margolis, S. E. (1994). Network externality: An uncommon tragedy. Journal of Economic Perspectives, 8(2), 133-150.
  30. Liebowitz, S. J., & Margolis, S. E. (1999). How Microsoft won the office race fair and square. Upside, 11(12), 185-205.
  31. Mintzberg, H. (1977). Policy as a field of management theory. Academy of Management Review, 1, 88-103
  32. Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competitors. New York: Free Press.
  33. Porter, M. E. (1981). The contributions of industrial organization to strategic management, Academy of Management Review, 6, 609-620.
  34. Porter, M. E. (2001). Strategy and the Internet. Harvard Business Review, 79(3), 63-78.
  35. Reinganum, J. F. (1983). Uncertain innovation and the persistence of monopoly. American Economic Review, 73(4), 741-748.
  36. Rosenberg, N. (1982). Inside the black box: Technology and economics. Cambridge, UK: Cambridge University Press.
  37. Rosenbloom, R., & Cusumano, M. (1987). Technological pioneering and competitive advantage: The birth of the VCR industry. California Management Review, 29(4), 51-76.
  38. Schendel, D. E., & Hofer, C. W. (Eds.). (1979). Strategic management: A new view of business policy and planning. Boston: Little Brown.
  39. Scherer, F. M. (1992). Competition for comparative advantage through technological innovation. Business & the Contemporary World, 4(3), 30-39.
  40. Schumpeter, J. A. (1950). Capitalism, socialism and democracy (3rd ed.). New York: Harper.
  41. Shapiro, C., & Varian, H. R. (1999). The art of standards wars. California Management Review, 41(2), 8-32.
  42. Sheremata, W. (1997). Barriers to innovation: A monopoly, network externalities, and the speed of innovation. Antitrust Bulletin, 42, 937-972.
  43. Sheremata, W. (1998). ‘New’ issues in competition policy raised by information technology industries. Antitrust Bulletin, 43, 547-582.
  44. Sheremata, W. (2004). Competing through innovation in network markets: Strategies for challengers. Academy of Management Review, 29(3), 359-378.
  45. Tushman, M. L., & Anderson, P. (1986). Technological discontinuities and organizational environments. Administrative Science Quarterly, 31, 439465.
  46. Usher, A. P. (1954). A history of mechanical inventions (2nd ed.). New York: McGraw Hill.
  47. Varian, H. R. (2005). Copying and copyright. Journal of Economic Perspectives, 19(2), 121-138.
  48. West, J., & Dedrick, J. (2006). Scope and timing of deployment: Moderators of organizational adoption of the Linux server platform. International Journal of IT Standards Research, 4(2), 1-23.

See also:

Free research papers are not written to satisfy your specific instructions. You can use our professional writing services to order a custom research paper on any topic and get your high quality paper at affordable price.

Like this post? Share it!

Need a Custom Research Paper?