Study Network Effects In A Competitive Context example essay topic

In many industries, the network of consumers using compatible products or services influences the benefits of consumption. Positive network effects arise when the consumer utility of using a product or service increases with the number of users of that product or service. The telephone system is a widely used example since it seems clear that the value of being part of the network rises as the network sizes increases. Consumption benefits can also arise in markets where a large customer network leads to increases in complementary products and services, which in turn, leads to increased consumer utility (e. g., see Farrell and Saloner 1985; Katz and Shapiro 1985; 1986). Prominent examples of industries thought to exhibit network effects include automated bank teller machines, computer hardware and software, videocassette recorders, video games, and Internet web browsers. Not surprisingly, network externalities and the implications of having a large installed customer base are receiving increased attention by strategy researchers (e. g., Gard and Kumaraswamy 1993; Hill 1995; Wade 1995).

As noted by Majumdar and Venkataraman (1998), the literature related to network effects broadly tackles three categories of research questions: (1) technology adoption decisions (e. g., what factors are related to whether and when a new technology is adopted), (2) technology compatibility decisions (e. g., what factors influence a firm's decision to seek compatibility), and (3) decisions among competing incompatible technologies (e. g., what factors are related to consumers' choices among rival incompatible products within a single product category). While theoretical research has addressed all three of these categories, empirical research has been limited to the first and second categories of questions (e. g., see the reviews in David and Greenstein 1990; Liebowitz and Margolis 1994; Economies 2001). Empirical efforts supporting the existence of network effects for a single product technology show that a larger network size is related to higher minicomputer sales (Hartman and Tee ce 1990), higher likelihood of adopting a new telecommunications technology (Majumdar and Venkataraman 1998), and quicker adoption of a new banking technology (Saloner and Sheppard 1995). In addition, Ganda l (1994; 1995) and Brynjolfsson and Kemer er (1996) use a hedonic price model to show that consumers are willing to pay higher prices for software products that are compatible with the dominant product standard, i. e., the product with the larger customer network. However, with the exception of a few industry case studies (e. g., Gabel 1991; Gridley 1995; Liebowitz and Margolis 1999), we are unaware of any published studies that empirically investigate the nature of network effects in an industry with multiple competing product technologies that are incompatible. Consequently, the purpose of this paper is to explore the third category of research questions that has received scant empirical attention, i. e., we investigate the possible network effects that might exist for a set of competing firms with incompatible product technologies.

This general situation is important since many markets have more than one product standard in equilibrium. For example, currently in the PC market there are three major operating systems (Windows, Mac and Linux) and in the cellular phone market there are three standards (CDMA, TDMA and GSM). Even the telephone system initially had multiple, competing networks that were incompatible (e. g., Mueller 1997). Voor tman (1993) provides an extensive list of industries with competing incompatible technologies. Important questions in this context include the following. Do network effects exist within each competing product technology?

What is the nature of these network effects? Are these network effects symmetric across firms? What are the implications of network effects on the outcome of competition among firms with incompatible technologies? In this paper, we address these questions in the 16-bit home video game industry in which the dominant players were Nintendo and Sega. Following prior empirical research dealing with network effects, we explore these questions in the context of a single industry. While this approach limits the generalizability of our results, it does allow us to explicitly study the dynamics of the competitive interactions between a set of firms.

Since our study deals with the competition between two dominant firms, we first develop a game theoretic model of competition. Using monthly time-series data for Nintendo and Sega, we then estimate the network effects for each firm via a structural econometric model assuming the data are equilibrium outcomes of the best fitting non-cooperative game in price and advertising. The contribution of our study is threefold: (1) we provide a theoretical framework for the possible role of network effects in an industry with competing incompatible product technologies, (2) we provide empirical evidence on the nature of these network effects and their implications for competition in a particular industry, and (3) we offer an approach to both analytically and empirically study network effects in a competitive context. The remainder of this paper is organized as follows. In the next section, we discuss how the Resource-Based View of the firm can be used as a theoretical framework for studying network effects and competition. To furnish a specific context for our model development, we provide some background on the home video game industry and the nature of competition in Section 3.

Then, we develop a game theoretic model of duopoly competition in advertising and pricing for the video game industry situation in Section 4. Given the nature of this industry, our model incorporates the possible effects of a firm's customer network. In Section 5, we discuss the data available for estimation purposes, the estimation results and our interpretation. In Section 6, we 4 discuss the implications of our findings, and in the last section we discuss the limitations of our study and outline some future research directions..