Introduction to Winning the Hardware-Software Game: Using Game Theory to Optimize the Pace of New Technology Adoption
- 0.1 Game Theory and the Chicken-and-Egg Problem
- 0.2 Organization of the Book
Have you ever had a piece of recorded music and no music player on which to play it? There you were, with your favorite Bob Dylan CD, and not a CD player in sight. Or maybe you have an old cell phone sitting in a drawer. It never rings because nobody knows it is there but you.
In both of these examples, you need both hardware and software or content or connection services (jointly referred to as software) to make the thing work. With only a CD and no hardware on which to play it, there is no music. With no connection services from a phone company, the phone doesn’t work either. In each example, either the hardware (the CD player) or the software (the phone service) is missing.
Different platforms such as the cell phone or disc player use different types of technologies to work. A cell phone needs a service provider, whereas a CD is a media source. In both cases, the hardware won’t work without this missing piece, what I call software.
As technologies evolve and become ever more complex, these hardware–software technology systems are popping up in ever more locations. These systems require consumers to simultaneously use both a hardware platform together with a software or content component to enjoy the consumption experience provided by the complete technology system. More examples of technology systems include
- Audio/video systems, including CDs, DVDs, TVs, and iPods
- Gaming systems, including Sony PlayStation; Microsoft Xbox; and Nintendo GameCube, Wii, and Game Boy
- Automobiles, which require fuel and maintenance
- Computer systems
- Communication systems, including facsimile machines, cell phones, and PDAs
- Transportation systems, which require vehicles (planes, trains, and automobiles, for example) and roadways (routes, tracks, and roads)
- Automatic teller machines
- Data collection and storage systems, including radio-frequency identification (RFID) systems, scanners, and smart cards
- The NYSE, NASDAQ, eBay, and any other system that requires system infrastructure and equities or products to trade or sell within them
In most cases of technology systems, users buy hardware from one company and software from other companies. For example, RCA, Sony, and Magnavox sell TVs, and various broadcast, cable, and satellite companies sell programming services. Sony, Panasonic, Emerson, and Philips sell CD players, whereas EMI, Warner Brothers, and Columbia sell music CDs. And Motorola, Qualcomm, and Samsung sell cell phones, and Verizon, Sprint, and T-Mobile sell local and long-distance services.
In these examples, in which separate companies provide the hardware and software components, the marketplace will adopt new or updated system technologies only when the actions of three different groups synchronize: hardware manufacturers; software, service, and media suppliers; and potential adopters. More specifically, hardware manufacturers are suppliers of the new system hardware, which includes any system software needed for the basic functioning of the hardware. In contrast, software providers are suppliers of new system software, accessories, services, or other content whose products enhance the value of the new system hardware, thereby attracting new users to the system. Both of these groups sell to the potential adopters, such as large companies, governmental agencies, or regular people like you and me.
When a new technology system is introduced into the marketplace, these three groups will act independently of one another and do whatever is in their own best interest. Hardware manufacturers will set prices to encourage potential adopters to upgrade to their new systems in a way that will maximize the manufacturers’ profits, regardless of what may maximize profits for software providers or optimize value for users. Similarly, software providers will continue to supply content for already existing systems, or they will switch over to providing content for newly introduced technology systems, whichever will earn them the most profits, regardless of what may be best for hardware manufacturers or users. And potential adopters will continue to use installed technology systems, or they will upgrade to newly introduced systems, whichever will give them the most value, regardless of what may generate the most profits for hardware manufacturers or software providers.
The adoption of high-definition television, or HDTV, is a good example. The adoption of HDTV by Americans has been remarkably slow, because the interests of the three sets of players—manufacturers of HDTVs, providers of program broadcasting, and viewers like you and me—have been at odds with one another. The manufacturers of HDTV sets would have liked to sell their TVs to viewers much more quickly than they have been able to, because they would have generated more profits with a speedier pace of adoption. The U.S. government wanted U.S. television viewers to adopt HDTV more quickly than they have, because it wanted to free up the spectrum currently being used for analog television broadcasting for other purposes. In other words, HDTV manufacturers and the U.S. government wanted the adoption curve for HDTV to follow the path of the curve on the left in Figure 0-1. Unfortunately for them, however, the reluctance of U.S. viewers and television broadcasters to upgrade to HDTV means the adoption curve for HDTV in the U.S. market looks more like the curve on the right in Figure 0-1. As it happened, though, it was in the broadcasters’ best interest to continue to supply old TV programming instead of upgrading to HDTV. Similarly, TV viewers decided that they would rather continue to watch their current analog (or non-HDTV-compatible) TVs instead of spending money to buy a new HDTV. The conflicting interests of the three groups have caused the new technology of HDTV to be adopted at a snail’s pace.
Figure 0-1 Technology adoption curves
Digital television, of which HDTV is a particular type, was introduced into the previously all-analog U.S. marketplace during the 1990s. “In 1997, Congress set a December 31, 2006, deadline for the transition to all-digital broadcasts.”1 Certainly, this deadline whooshed past before everyone adopted HDTV. Consumers have been slow to upgrade their hardware from the traditional analog technology because prices of HDTVs have been too high to justify buying them because of the minimal amount of digital programming available. The hassle and cost of upgrading to HDTV, including getting rid of the old set, outweigh the sparse benefits for most people. For their part, TV stations have been slow to provide digital broadcasts because of the high cost of changing from analog to digital, especially to supply only a few viewers who have HDTVs.
Was there any way HDTV manufacturers could have sped up the pace of adoption of HDTV in the U.S. marketplace? That is precisely the question this book seeks to answer.
0.1 Game Theory and the Chicken-and-Egg Problem
I use game theory to figure out how to speed up the pace of adoption of new system technologies like HDTV. To do this, I need to better understand the incentives each entity (hardware manufacturers, software providers, and users) faces when a new technology system is introduced into the marketplace. This leads me to turn to game theory in search of some answers. Game theory examines situations in which entities
- Act with, and react to, each other, independently from and strategically with one another, doing what is in their own best self-interest
- Receive payoffs that are dependent upon the actions of the other players
The concepts underlying game theory have been used since time immemorial:
- The ideas underlying game theory have appeared throughout history, apparent in the bible, the Talmud, the works of Descartes and Sun Tzu, and the writings of Charles Darwin ... While many other contributors hold a place in the history of game theory, it is widely accepted that modern analysis began with John von Neumann and Oskar Morgenstern’s book, Theory of Games and Economic Behavior  and was given its modern methodological framework by John Nash building on von Neumann and Morgenstern’s results.2
Since von Neumann and Morgenstern’s work in the 1940s, the game theory framework has become more widely and deeply applied to the point that “game theory is a sort of umbrella or ‘unified field’ theory for the rational side of social science, where ‘social’ is interpreted broadly, to include human as well as non-human players (computers, animals, plants).”3 More recently, game theory has been used to understand behavior in biology, economics, politics, computer science, philosophy, sociology, and psychology,4 to such an extent that
- since at least the late 1970s it has been possible to say with confidence that game theory is the most important and useful tool in the analyst’s kit whenever she confronts situations in which what counts as one agent’s best action (for her) depends on expectations about what one or more other agents will do, and what counts as their best actions (for them) similarly depend on expectations about her.5
In the HDTV game, hardware manufacturers act by manufacturing specific numbers of television sets and setting their prices. They react to software providers and viewers by providing fewer sets and/or charging lower prices when there is not a lot of HDTV programming available and/or viewers are not buying HDTV sets. Software providers act by choosing to provide none, some, or a lot of HDTV programming. They react to viewers by increasing the amount of HDTV programming available as more users adopt HDTV or by charging more for HDTV programming. Viewers act by choosing either to stay with their analog systems or to upgrade to HDTV. They react to hardware manufacturers by upgrading faster when the price of HDTV sets is lower, and they react to software providers by upgrading faster when there is more digital programming and when the price of digital programming is lower.
These three sets of players do what is in their own self-interest—that is, what will maximize their own profits or value—regardless of what might be best for either of the other two sets of players. Ultimately, though, the amount of profits HDTV manufacturers and television broadcasters generate and the amount of value viewers receive will depend on the actions the other two sets of players take, that is, the prices hardware manufacturers charge, the amount of programming broadcasters make available, and the timing of users’ adoption.
In essence, then, this problem of trying to coordinate the actions of independent (1) providers of system hardware and (2) providers of system software with (3) potential users is actually a strategic game that plays out when the new Xbox hits the market, Apple introduces a new gadget, or more generally, anytime new system technologies are introduced into the marketplace.
The problem that often appears in these hardware–software games is the classic chicken-and-egg problem: Software and media providers are reluctant to supply content early on because too few users have adopted the technology systems to make the new markets profitable for software providers to enter, and users are reluctant to adopt the new systems early on because there is not enough content to make their consumption experiences worthwhile. The chicken-and-egg problem can result in long delays before technology systems are successfully adopted, if adoption is not completely stymied altogether.
For system technologies, the trick, then, is to get all three sets of players to play the game at the same time. The hardware manufacturers must provide the new technology, and at the same time the content providers must find it profitable to supply content for the new technology, and at the same time users must find it worthwhile to adopt the new technology. Only when all three players are simultaneously on the same page will the technology become adopted at a reasonable pace.
There has been a variety of previous studies of the adoption paths of hardware– software system technologies, but the studies tend to examine adoption paths of specific technology systems on a case-by-case basis, providing insights about the particular cases studied. For example, it has been established that new systems that are compatible with existing systems tend to get adopted much more easily, as do new systems that provide a new “killer app.” However, there is scant research either on the economic structure of the general dynamics between hardware and software suppliers itself during the early stages of adoption of new technology systems, or on methodical or systematic analyses of how hardware providers might speed up adoption of their new systems under various situations and/or market scenarios.
And that is where this book comes into play. Specifically, this book examines the holdup problem encountered during the adoption process of a particular class of new technology systems. The class consists of systems in which (1) both hardware and software components are required to produce the consumption experience for users of the technology systems, (2) the hardware and software components for the technology systems are supplied by providers who act independently from one another, and (3) the technology systems exhibit network effects. I refer to the interactions of these independent suppliers of hardware and software components during the course of introduction of new technology systems as the Hardware–Software Game.
This book proposes to provide a systematic analysis of the structure of the Hardware–Software Game. From this comprehensive analysis, I generate insights for different systems and market scenarios. I use a simulation model based on game theory to examine the economic structure of the Hardware–Software Game, to see what is in the best interest of each set of players, to determine if and when they will be led to adopt a new technology system on their own, and to see what the hardware manufacturers can do to get their systems adopted sooner rather than later. Once the structure of the game has been examined, it will be easier to understand such issues as the following:
- Why do some great systems get adopted, whereas others do not?
- Which forces drive adoption by users and content providers?
- How can system innovators speed up the pace of adoption of their new technology systems?
After analyzing the structure of the game and better understanding the underlying dynamics, I am then able to propose means by which new system innovators can help speed up the pace of market adoption of their new systems.