bandwagon effects in ict diffusion

Working Paper:

The Role of Bandwagon Effects in Information and Communication Technology Diffusion

by Otto Nyberg Aalto University – School of Science and Technology

7th of April 2010

Keywords: Technology diffusion, bandwagon effect, positive consumption externality, network effect, ICT, mixed model of influence.

AbstractInformation and communication technology markets have a strong tendency towards lock-in. The research question this paper focuses on is what makes ICT markets especially prone to lock-in. In exploring the topic, a number of bandwagon effects are identified and their functioning are illustrated. These need to be combined in order to achieve a more complete understanding of the ICT markets. There are some models of mixed influence available, but these are based mostly on theoretical work that lack verification by empirical results. The models presented so far remain hypotheses as they are not validated.In this paper three distinct bandwagon effects are identified and combined to a model of mixed influence to explain diffusion of ICT. The model created is validated by findings in empirical research. Research results in the field from all econometric studies known to the author are collected and combined with some indicative research results to provide context and magnitude of impact of bandwagon effects. As there are varying practices about vocabulary in this context, this paper is also an attempt at clarifying the vocabulary to be used in the field of bandwagon effects.

O. Nyberg / Bandwagon Effects in ICT Diffusion

Table of ContentsAbstract......................................................................................................................................................1

1 Introduction............................................................................................................................................4

2 Bandwagon effects in information and communication technology industries.....................................5

3 Existing literature...................................................................................................................................6

3.1.1 History and theoretical work..................................................................................................6

3.1.2 Econometric research .............................................................................................................7

3.1.3 Case studies............................................................................................................................7

3.1.4 Models of diffusion.................................................................................................................8

3.2 Purpose of this paper......................................................................................................................9

4 Concepts.................................................................................................................................................9

4.1.1 Lock-in....................................................................................................................................9

4.2 Taxonomy of bandwagon effects....................................................................................................9

4.2.1 Herd behavior.......................................................................................................................10

4.2.2 Informational bandwagons...................................................................................................10

4.2.3 Indirect network effect..........................................................................................................11

4.2.4 Direct network effect and network externality.....................................................................11

4.3 Other factors.................................................................................................................................12

4.3.1 Stand alone value of technology ..........................................................................................12

4.3.2 Learning effect......................................................................................................................12

4.3.3 Technology evolution...........................................................................................................12

4.3.4 Price evolution......................................................................................................................13

4.3.5 Marketing..............................................................................................................................13

5 Mixed macro model of technology diffusion.......................................................................................13

5.1 Mixed model – the combined effect of bandwagon effects..........................................................13

5.1.1 Individual adopters with individual thresholds.....................................................................14

5.1.2 Increasing bandwagon pressure and perceived value...........................................................15

5.1.3 Technology evolution...........................................................................................................16

5.1.4 Price evolution......................................................................................................................17

5.2 The S-curve..................................................................................................................................17

– 2 –


5.2.1 Adopter threshold distribution and bandwagons..................................................................18

5.2.2 Adopter threshold distribution and technology evolution....................................................18

5.2.3 Adopter threshold distribution and price evolution..............................................................18

5.2.4 The complete model.............................................................................................................18

6 Empirical findings on the impact of bandwagons................................................................................20

6.1.1 Spreadsheet markets.............................................................................................................24

6.1.2 Database markets..................................................................................................................24

6.1.3 VCR's, CD's and video games..............................................................................................24

6.2 Implications for the model presented...........................................................................................24

7 Conclusions..........................................................................................................................................25

7.1 Future research.............................................................................................................................27

7.2 Acknowledgments........................................................................................................................27

References...............................................................................................................................................27

– 3 –


1 IntroductionTechnological innovation has acquired a central position in business press, at least in Finland, during the past years. After the financial crisis in 2008-2009, policymakers have been looking for ways to improve growth. According to Schumpeter's theory of economic development (Schumpeter 1934) this growth can come from innovation. The rent on invested capital, that many take for granted, is dependent on this economic growth which in turn implies that e.g. pension funds' functioning are dependent on the very same. Real (inflation adjusted) economic growth can come from three sources: population growth, more work per capita (longer working hours, more intensive work, and/or longer professional career), and innovation. Successful innovation in itself leads to more valuable output per input effort and material, thus increasing productivity. As the population growth has virtually ceased in Western countries, we are left with more work per capita and innovation as drivers of growth. In some countries (e.g. Sweden) the government is hoping that immigration will make up for this seizure in population growth caused by the slow reproduction of the native citizens. Others are taking measures to increase the length of active professional careers by both pushing people to move into the professional life at an earlier age and by postponing the retirement age (e.g. Finland). The third action that can be taken is to affect innovation itself. This is done in many countries e.g. through governmental institutions with the purpose of facilitating entrepreneurial activity. This illustrates a central characteristics of modern culture: in order to preserve the culture in short term, continuous economic growth is needed, which in turn produces cultural change in the long term. It seems that change is the only thing that is constant.Another significant change is that traditional supply and demand economics do not dominate the functioning of all markets anymore (e.g. Abrahamson & Rosenkopf 1997; Farrell & Saloner 1992; Katz & Shapiro 1994). In some markets, the demand is not only a function of price; in several markets the demand is affected by the current number of adopters thus creating a positive feedback loop. This tendency has been recorded in markets for e.g. banking services (Shy 2001), electric household appliances (Bulte 2000), and technology in general (Shy 2001; Katz & Shapiro 1986; Bresnahan 2003). This tendency is particularly strong in markets where network externalities are present, where concepts such as installed base, de facto standard and lock-in define competitive action and drive diffusion. These concepts are strongly present in technology markets.The point is that free market economy does not necessarily produce efficient results in these markets. Better understanding of the diffusion drivers and process hopefully enable policymakers to make better policy decisions and managers to make better estimates and more profitable decisions, thus producing more efficient results in the prevailing market economy.Innovation diffusion research can be split in macro and micro levels. Research on the macro level is concerned with finding variables that can be generalized to explain diffusion from a probabilistic perspective for large populations. Research on the micro

– 4 –


level seeks to explain individual decisions where many of the theories involve information models and social networks (Valente 1996). Micro factors probably play a significant role in early markets, but once the tipping point has been reached, it seems that macro factors determine the future of an innovation. Even though small differences in early markets tend to have significant impact on the future of innovations (Rogers 2003; Gladwell 2000), the macro level should not be disregarded in early markets. Any one of the macro level drivers might cause the diffusion to stop on the micro level in early markets, but controlling these drivers is not enough to guarantee continued diffusion. It seems that early market diffusion requires more than good management. Some keys to early market diffusion can be found in the micro level, but that is left out of scope for this paper. The cumulative diffusion of technology over time forms the well known logistic S-curve (Phillips 2006; Bulte 2000). Understanding the drivers that make people and/or organizations as a whole produce this logistic S-curve through adoption decisions could provide valuable information for at least policymakers and business managers. There are many types of innovation, e.g. organizational, managerial, strategic and technological (Abrahamson & Rosenkopf 1993), but this paper will focus on technological innovation alone. More precisely, the bandwagon properties of ICT innovations will be explored. These are frequently mentioned as the forces that make ICT markets prone to lock-in giving their suppliers monopoly-like bargaining power. For company managers, this is one identified potential source of sustainable competitive advantage; for policymakers, this is one identified source of market failure.The lock-in concept will be explored and used as a measure of bandwagon effect impact to illustrate the drivers behind technology diffusion. The focus will be on macro level intrinsic drivers, but some parallels to other factors present will also be made. The S-curve will be used to illustrate some implications.This focus seems to imply an assumed rational-efficiency theory of diffusion, whereas in reality the implications apply for mixed diffusion models.The effect of market competition on diffusion is left out of scope. This limitation equals studying the diffusion of one innovation in general without focusing on one company's product, or a situation where there is only one company providing the innovation. Branding and marketing are also left out of scope as the focus is on explaining what makes technology markets different from other markets.

2 Bandwagon effects in information and communication technology industries

Cases where the previous decision makers' choices affect consecutive decision makers' choices have long since been documented, e.g. elections and fashion in the 1950's (Leibenstein 1950), and telephone networks in the 1970's (Rohlfs 1974). The concept of

– 5 –


positive feedback and bandwagon effects is thus not very new. What is new is that similar effects are seen in increasingly many high technology products and services, especially in ICT. Economists and managers have traditionally assumed a demand inversely related to price, where lower price would yield more sales and higher market share. This assumption still seems to be fairly valid in traditional industries where networks and complements play only minor roles, but this assumption is loosing ground. In today's world, many products and services no longer follow this model. In the digital world of today many services and products are produced for an insignificant marginal cost. As a consequence, the price for these products might in a competitive market be so small that other factors than price might dominate customer choice. Many products protected by intellectual property rights are such products, e.g. literature, computer programs, and music. All of these examples provided are surrounded by indirect network effects (Katz & Shapiro 1994; Rohlfs 2001), a type of positive feedback loop where the increasing number of users cause higher value for all users. One step stronger effect is noticed in communication technology products where large parts of the customer value lie in the communication network itself (Katz & Shapiro 1985). The value of a communication network is usually seen to be dependent on the number of subscribers; adding one subscriber to the network increases the utility of the network for all subscribers. The traditional example considers fax machines: being the only owner of a fax machine is quite useless, but as more and more people acquire fax machines, the utility of owning one and “being part of the network” increases. This is just one example of externalities in communication technology markets. The important part is to notice that demand is no longer simply a function of price. Simply stated, demand in these circumstances are better seen as a function of price and installed base, expected installed base, and similar. This has substantive implications - for legislation as well as for strategy, business models, and pricing - both for existing competitors and new entrants. These forces could be seen as acting on both micro and macro levels. This paper explores forces on the macro level where probabilistic generalizations for larger populations can be made. Positive feedback loops relating to properties of the technology are better explained on this level. In contrast, the micro level deals with adoption decisions of individuals and is more concerned with social networks and properties of individuals (Valente 2005; Abrahamson & Rosenkopf 1997; Valente 1996). The herding behavior can be explained in more detail on the individual level, but this is unnecessary detail for a bandwagon effect that is fairly simple to explain and understand also on the macro level.

– 6 –


3 Existing literature

3.1.1 History and theoretical workTechnology diffusion is a field dealing with the spread of technologies in society and the evolution of technologies. The diffusion of technologies is highly affected by the positive feedback loops that are the focus of this paper. The field has evolved from the domain of innovation diffusion which started in the 1940's with a study on diffusion of hybrid corn in Iowa (Bryce & Gross 1943). Later on, Everett Rogers collected all diffusion research in the book “Diffusion of Innovations” (Rogers 2003). The first edition was published in 1962 and he has published revisions regularly since, now reaching the fifth edition. Rogers has contributed enormously to the domain in question. Unfortunately for technology diffusion researchers, most of his research covers diffusion of agricultural innovations and innovation diffusion in developing countries which is rather far from the diffusion of high technology. For the field of technology diffusion, Rogers research probably provides most insight for the micro level where social factors, such as persons' characters and social networks, have dominant roles in explaining adoption decisions and consequently diffusion.In the 1980's and 1990's lots of theoretical work was done on the subject of network effect. There are two author-pairs that deserve to be mentioned specifically: Farrell & Saloner, and Katz & Shapiro. Farrell & Saloner published a paper in 1985 (Farrell & Saloner 1985) with the title “Standardization, Compatibility and Innovation” where they argue that excess intertia (lock-in) occur in some industries due to network effects. Katz & Shapiro explore similar ideas in their paper titled “Network Externalities, Competition, and Compatibility” (Katz & Shapiro 1985) and find that private profitability and social welfare concerns are not always aligned in industries where network effects (externalities) are present. These four authors have continued research on the topic and published results produced mainly by analyzing mathematical models and running computer simulations (e.g. Farrell & Saloner 1988; 1992; 1966; Katz & Shapiro 1986; 1986; 1992). These papers contain important theories with implications both for competitive action and legislation. The theories explain the diffusion of technologies on the macro level where network effects are present. Since the 1990's there has been some research focusing on the micro level trying to explain the decisions of individual decision makers and the factors affecting their decisions. Abrahamson & Rosenkopf published a paper in 1997 where they using computer simulations showed that the structure of a social network might influence the diffusion of innovations largely (Abrahamson & Rosenkopf 1997). Today focus seem to be on the micro level explaining individual decisions which is seen as more relevant in early market diffusion where standards have not been set and the market has not tipped (Gladwell 2000) in favor of any competitor or standard. Much of this work in the 1980's and 1990's is based on mathematical modeling and computer simulations, and has not yet been validated by empiric research.

– 7 –


3.1.2 Econometric research The econometric research on bandwagon effects in technology diffusion is fairly limited compared to purely theoretical research. Some reasons often mentioned are the difficulty to separate bandwagon effects from other simultaneous effects, e.g. technology evolution and scale economies, the rapid price erosion in technology industries, and the need for both time-series and cross-sectional data (e.g. Gowrisankaran & Stavins 2004). There is a small number of econometric studies covering the diffusion of information and communication technology (ICT) which involve bandwagon effects (e.g. Brynjolfsson & Kemerer 1996; Saloner & Shepard 1995; Gandal 1994; Gowrisankaran & Stavins 2004). These are covered in the section “6. Empirical findings on the impact of bandwagons.”

3.1.3 Case studiesThe existing empiric research consists mostly of case studies. The history and evolution of the computer software industry is quite well summarized in two books, “Folded, Spindled, and Mutilated” (Fisher et al. 1983) and “From Airline Reservation to Sonic the Hedgehog” (Campbell-Kelly 2004). The book by Fisher & al. covers the evolution of both hardware and software from a market competition and antitrust perspective. The book is built on evidence from the U.S vs. IBM antitrust-case (1969-1982) which deals with many topics similar to those dealt with in the case U.S. vs. Microsoft (Bresnahan 2001). The book by Campbell-Kelly covers the evolution of the software industry starting from the 1950's, when software was not yet distinguished from hardware, to around year 2000 when Sega's most popular game featured “Sonic” (the hedgehog). Tim Bresnahan has covered the last ten years of the IT product industry in a number of papers based largely on evidence from the antitrust cases brought against Microsoft (e.g. Bresnahan 2001; 2003). These together present a picture of a rapidly evolving industry. The striking things, though, are the similarities found over time despite the fast pace of the industry; segments in the IT industry have a strong tendency towards lock-in and all these authors attribute this to network effect.Clayton Christensen & al. has studied mainly the computer hardware industry during the 1980's and 1990's (Christensen & Raynor 2003; Christensen 1999; Christensen et al. 2004) and argue that technological industries evolve in fairly predictable incremental stages interrupted by disruptive change. Another author dealing with the topic of technology evolution from a slightly different perspective is Geoffrey Moore (Moore 1991; 1995). Moore studied how the buyer side in technological markets evolve, and argues that there is an important difference between the needs of those ones adopting first (innovators) and the next adopter category (early adopters). Both Christensen's and Moore's findings relate mostly to technology evolution and competitive strategy. A case collection by Jeffrey Rohlfs is focusing more on high technology in general with different types of bandwagon effects (Rohlfs 2001). Rohlfs book is a case collection covering diffusion of technological products such as televisions, picturephones, VCR's, and CD's.

– 8 –


3.1.4 Models of diffusionModels of innovation diffusion have been suggested by many. Most models have an emphasis on one specific explaining factor which makes these models thorough in explaining the functioning of one factor. Barbara Wejnert provides a good overview of sociological models in her 2002 paper (Wejnert 2002), but the coverage of rational-efficiency theories is light and she does not cover bandwagon models. One-factor models have a narrow focus which makes them poor at explaining diffusion in more than one or just a few cases. Models of mixed influence try to fill this gap. There are only a few models of mixed influence to explain technology diffusion. Abrahamson & Rosenkopf provide a high-level model for bandwagon effects, implicitly implying that there are several forces acting simultaneously (Abrahamson & Rosenkopf 1997). They do not deal with how these bandwagon effects interact, as the focus of their paper is on explaining the effects of social network structure on diffusion. Geroski covers a number of technology diffusion models and uses these to draw some implications for policymakers (Geroski 2000). The goal of his paper is not to create a mixed model; he uses his model to argue that technologies do not typically diffuse too slowly from a social welfare point of view. Fred Phillips explains technology diffusion on a general level by a three-factor model (Phillips 2006). The model is an extension of the Bass-model (Bass 1963) and its value lies in its simplicity of application. The model does not map to technological properties of the innovation and is very general in nature. Melissa Schilling presents a model that is based on rational-efficiency (p. 73, Schilling 2008). Technological utility is split into stand-alone value, installed base, and complementary goods availability, but e.g. herding is not covered. The models available are mostly an aid to understanding; they present a structural view of the technology diffusion process. Interactions and impact magnitude of different forces remain unexplained.

3.2 Purpose of this paperWhat all these authors find is that information and communication technology markets in general, and some other high technology markets, exhibit network effects and possibly some other types of bandwagon effects. There is compelling evidence that ICT markets in general have a tendency towards lock-in and this is attributed to bandwagon effects. What is lacking is an understanding of what impact these effects have. As mentioned earlier, there are many problems with distinguishing the different effects in a dynamic environment (Brynjolfsson & Kemerer 1996; Saloner & Shepard 1995) but there is nevertheless some empirical evidence. A model of mixed influence is developed in this paper to show how these bandwagon effects interact, and the empirical evidence available is collected to verify their existence and to convey an understanding of their impact magnitude and effect in different contexts.

– 9 –


4 ConceptsThe vocabulary used in this field varies and is sometimes imprecise. To avoid confusion, some concepts used in this paper are clarified below and a taxonomy for bandwagon effects is presented.

4.1.1 Lock-inSome literature have used lock-in as evidence for existence of bandwagon effects in markets. In this paper the term is used mainly to understand existing findings.Oz Shy defines lock-in as the situation where a superior technology is not taken into use because its stand-alone value is not higher than an existing technology's stand-alone value plus its network value (Shy 2001). This definition was good enough for Shy to explore social welfare implications of the network effect. In this paper, a definition by Jeffrey Rohlfs is used. Rohlfs defines lock-in as the size of switching costs making some users unwilling or unable to switch to a superior technology. These costs can sometimes be contractual, sometimes structural (Rohlfs 2001). Thus lock-in occurs when customers are unwilling or unable to switch to a superior technology. Usually it is seen as a macro economic problem when users would be better off if everyone switched technology. Rohlfs' definition is based on the customer point of view and further has the advantage of being measured in money. It also acknowledges lock-in caused by other forces than network effect.

4.2 Taxonomy of bandwagon effectsThe term bandwagon effect is used here as an umbrella term, even though there are varying practices about the vocabulary to be used in this domain. The forces falling under this term are all dependent on the current or the expected future number of users, also referred to as adopters, where the addition of one user increases the likelihood for current users to stay and for potential users to adopt. This is a positive feedback loop; adding users makes the producer better off, increases the current users' valuation of adoption and increases the likelihood for new users to adopt. The same definition is used by Abrahamson & Rosenkopf (Abrahamson & Rosenkopf 1997) and Rohlfs (Rohlfs 2001), but there are also some authors (e.g. Herpen et al. 2005; Leibenstein 1950) who use the term bandwagon for what is referred to as herd behavior in this paper (see section below), which is only a subgroup of all bandwagon effects. Another thing that bandwagon effects have in common is that producers cannot directly affect them; they are in a sense external to the producer's influence, although intrinsic to the innovation.

4.2.1 Herd behaviorThe term herd behavior is used when the sheer number of users, rather than e.g. updated

– 10 –


information on profitability, makes potential users more likely to adopt and current users more likely to stay. This definition is used by e.g. Abrahamson & Rosenkopf (Abrahamson & Rosenkopf 1997) and some other scholars (Scharfstein & Stein 1990; Banerjee 1992). Saloner & Farrell used the term penguin effect in 1966 to describe the same phenomenon (Farrell & Saloner 1966). In 1998, Mehrabian found in an experiment that up to 6% of voters changed their opinions to the advantage of the winning candidate after seeing a poll report (Mehrabian 1998).There has been some speculation on the sources of herd behavior, some scholars credit it to human nature (Takeyama 1994; Leibenstein 1950), some say it is because of fear of falling behind or fear of appearing different (Abrahamson & Rosenkopf 1997), whereas some speculate that it has to do with the notion that “the masses are right.” All explanations seem to have some validity in different context.Closely related to herding is its opposite; the Veblen effect (Leibenstein 1950). Veblen effect is the decrease in demand for products like fashion clothing where a large number of users cause users to discontinue adoption and decreases the willingness of potential adopters to adopt. This paper will not further deal with the subject of Veblen effect as it does not seem to be relevant in contemporary technology diffusion.

4.2.2 Informational bandwagonsInformation has, according to theory, two different roles in bandwagon effects. Rogers suggested a model for information flow, where not knowing about an innovation is the main reason preventing adoption and diffusion (Rogers 2003). He argued that information flows through interpersonal communication where one person tells a few others, who in turn tell a few etc. causing an exponential growth in awareness which supposedly would explain the beginning of the S-curve. Rogers' model is perhaps best suited for agricultural and family planning innovations in developing countries, but it also has some credibility as explanation for slow diffusion of disruptive innovations. Abrahamson & Rosenkopf presented a similar model where low reliability of the information was the reason for not adopting (Abrahamson & Rosenkopf 1997). The difference between the two models is that Rogers stressed the social interactions as the key to diffusion, whereas Abrahamson & Rosenkopf emphasized that every adopter creates new information which enable potential adopters to more precisely evaluate the utility and the risk involved in adopting an innovation. In both cases, the reason hindering a potential adopter from adopting is the lack of information. Abrahamson & Rosenkopf also sorted bandwagons caused by knowledge as a complement to a product under informational bandwagons. An example would be programming languages as complements to development platforms, although empiric research has not found evidence for such a bandwagon (Gandal 1995). The topic will further be dealt with in the section “4.3.2 Learning effect” (see below).

– 11 –


4.2.3 Indirect network effectIndirect network effect is a rather large group of bandwagon effects. These bandwagon effects are rational efficiency theories where the increase in utility of the service or good is explained by a change in the surrounding environment (network). Indirect network effect theories assume that as a new product or service emerges, the market environment will adapt and start producing complements. The market thus evolves into a value net (Parolini 1999) where companies become dependent on each other and the users benefit from a better offering in terms of plurality and/or quality (Dhanaraj & Parkhe 2006). In these cases, once a user invests in one network, her willingness to switch to a competing network decreases because of bound capital and switching costs. The utility of belonging to this network increases for both users and potential users. The positive feedback loop is thus present which makes this force sort under the umbrella term bandwagon effect. The term is called indirect network effect in this paper. The terms complementary bandwagon effect and complementary network effect are also used in literature for this effect. This is the type of bandwagon Katz's & Shapiro's and Farrell's & Saloner's papers from 1985 focus on (Katz & Shapiro 1985; Farrell & Saloner 1985), although they focus on the consequences for political economy rather than the effect itself.As this is a rather large group of bandwagon effects, some examples are listed to further clarify the contexts where this effect is present. The most relevant case for this paper is the indirect network effect related to information technology. This indirect network effect in IT is in many cases built up by pairs: hardware and software (Bresnahan et al. 1997), software and file formats (Brynjolfsson & Kemerer 1996), server and client (Gallaugher & Wang 2002), but there are also other examples. Other examples concerning technologies involve VCR's and videos, CD's and CD-players, gaming consoles and games... There are some interesting points to learn from these. Some less obvious examples are electricity and electrical appliances, ATM's and credit cards, cars and gas stations. The list goes on. The interpretation of the term indirect network effect in this paper is rather wide. The basic idea is that there is a cross dependency of two or more goods or services or other type of complement where the switching costs for one is also considered switching costs for the other. The user is actually buying a system which comprise of several parts often provided by different vendors.

4.2.4 Direct network effect and network externalityAn externality in economics is a cost or benefit imposed on someone not taking part in a transaction. In a sense, the transaction is “leaking” and all implications of the transaction are not constrained to the parties involved. Externalities can be either positive or negative, where the positive ones are providing value to parties not involved in the transaction whereas the negative ones impose costs on them. An example of a negative externality is pollution. Network externalities are externalities relating to some network, and they are always positive. The classic network externality example is about fax machines: being the only

– 12 –


one owning a fax machine is rather useless, but as the number of faxes and fax users grow (the network) the utility of having a fax machine increases. There are actually two economic notions that can be separated here: the increased utility of owning a fax machine, and the increased utility of adopting for someone not having adopted already. The first part clearly is an externality whereas the second is not. In such a case, the term network effect is used to catch both these effects at once. There has not yet been an example of network externalities taking place without network effect, and these two concepts are often used as synonyms. Only for the clarity of the concept externality is this separation useful. Direct network effect differs from indirect network effect in the sense that the externality is working in a direct way. The increased utility from adoption flows to users directly without flowing through secondary nodes such as increased offering of complements, e.g. CD's. Communication technology always involve direct network effect, whereas in the case of indirect network effect, the effect have to flow through several nodes in the network before returning to the user.Direct network effect sorts under rational-efficiency theories of bandwagons where the bandwagon pressure is explained by increased utility. There seems to be an assumption that the impact of direct network effect is order of magnitude stronger than the impact of other bandwagon effects, but there is no empirical evidence supporting nor rejecting this claim.

4.3 Other factorsSome of the following factors also have positive feedback, but they do not create feedback loops in the same sense that the bandwagon effects do. What distinguishes these factors from bandwagon effects is that the addition of one user does not ceteris paribus increase the likelihood for other users to adopt, and these forces are under the direct influence of the producer.

4.3.1 Stand alone value of technology A technology itself often has a stand-alone value that is independent of any network. For some individuals, this can be enough to cause adoption. In the diffusion of VCR's, this largely helped in solving the start-up problem. VCR's were originally used for “time-shifting” to record TV-programs for later viewing. Some users bought VCR's only for time-shifting before the video rental business emerged. The emergence of video rentals brought the industry under the influence of indirect network effect.

4.3.2 Learning effectAfter users have invested time and effort in learning how to use one technology, they are often unwilling to sacrifice more time on learning how to use a competing technology (Rohlfs 2001). The effect of this factor is very local and does not spread in a bandwagon

– 13 –


manner, but nevertheless induces a switching cost and can at least be a contributing factor to lock-in.

4.3.3 Technology evolutionTechnology has historically improved over time. This improvement attracts more users which in turn, assuming that the producer has a profitable business model, generates higher profits that could be used for further development. This is not to be confused with a bandwagon, where the addition of one user ceteris paribus makes other users more likely to adopt. Technology evolution usually follows an S-curve where the horizontal axis represents the money invested in research and development (R&D), and the vertical axis represents the performance of the technology (Schilling 2008). There is thus a high payoff of investment in R&D in the early life cycle of a technology.

4.3.4 Price evolutionHigh technology markets are well known for rapid price erosion. E.g. Brynjolfsson & Kemerer found that spreadsheet software prices declined at an annual pace of 16% between 1987 and 1992 (Brynjolfsson & Kemerer 1996). PC hardware is known for even faster price erosion where one computer after 3 years of use typically collect only 10% of the original purchasing price. Production technology evolution makes production of existing technologies cheaper over time, and in case of successful diffusion, scale economies will make unit production costs lower. Some business models rely on price discrimination in the sense that the producers initially set the prices high to capture a larger part of the consumer surplus. This is not always the case in ICT industries with bandwagon effects; the bandwagon effects might be expected which could make the producers set the initial price low to gain market share early on in a technology's life cycle.

4.3.5 MarketingThe role of marketing cannot be excluded in this context. The Bass-model depicts that tipping has occurred when the totality of bandwagon effects is stronger than the effect of marketing. The model thus assumes that the impact of bandwagon effects are same order of magnitude as the impact of marketing. Marketing as such is a huge domain and not in scope of this paper.

5 Mixed macro model of technology diffusionAlthough all these factors are relevant in explaining technology diffusion, I consider them to be valid predictors only after the tipping point. I think that they play a role before the

– 14 –


tipping point in early markets, but it seems that individual decisions and qualitative assessments are more significant in determining early market diffusion (Gladwell 2000). Such factors are dealt with in micro-level research. Nevertheless, models such as the one developed in this paper should not be ignored in markets before the tipping point.

5.1 Mixed model – the combined effect of bandwagon effectsThis paper presents a model of mixed influence to explain the diffusion of ICT at the macro level. Following the models used by Schilling (Schilling 2008) and Geroski (Geroski 2000), this paper proposes a mixed model where the value of a technology is comprised of a stand-alone value plus the sum of different types of bandwagon values. The totality of these values needs to exceed a threshold specific to every individual for adoption to take place. Once diffusion has started, the perceived value has to increase continuously to exceed the following adopters' thresholds to make them adopt in order for diffusion to continue. One illustrative case is the spreadsheet market with the bandwagons surrounding it. Spreadsheets are used as a means of communication which implies a direct network effect. In addition, spreadsheets are complements to database software (Gandal 1995), thus showing an indirect network effect. The effect of herding has not been measured in the spreadsheet market, thus the impact of herding remains unknown. This example implicates a need for a model of mixed influence.

5.1.1 Individual adopters with individual thresholdsEach individual has an adoption threshold which is approximated by the price the individual is prepared to pay. Ideally this threshold would equal the utility of a technology for every individual, but this model only assumes that these form a distribution. Following Katz & Shapiro (Katz & Shapiro 1985), the minimum assumption needed is a uniform distribution with a minimum and a maximum threshold. To illustrate the robustness of the model proposed, a less strict assumption is also explored. Following Geroski (Geroski 2000), individuals' adoption thresholds are also represented as a normal distribution.

– 15 –


Chart 1. Adopter threshold distribution approximated by a bell curve following Geroski. Katz & Shapiro assumed a uniform distribution instead which is here visualized with the dotted line. Note that neither is contradictory to traditional demand curves, where the cumulative number of adopters is visualized as function of price.Often, adopter distribution is plotted over time, instead of threshold valuation as done here. As is later discussed in this paper, innovations change over time and so does their perceived value. Rogers used a classification where individuals were grouped based on their innovativeness where an early adopting individual were seen as being highly innovative (p. 272, Rogers 2003). The only difference in this model is that innovativeness is substituted by the threshold value.With these assumptions, chart 1 actually visualizes the same adopter distribution, but the early adopters are placed to the right instead. The interpretation of the graph is that there are few individuals who value some specific innovation highly, these are plotted to the far right, then the vast majority values the innovation quite closely to some average value, and then there are a few individuals that perceive the value of the innovation as very small or negative. These individuals are not likely to adopt voluntarily ever.An alternative distribution is the uniform distribution visualized in chart 1 as a dotted line. This would imply that there is not one price preferred by the vast majority, but that the threshold valuations are distributed evenly between some minimum and maximum valuation points. For the model in this paper, anything in between these two is sufficient.Some technological innovations are today provided for free, e.g. web services. In these cases, the threshold valuation should be seen as the value of the effort to learn how to use the service. Such a service is not entirely free either, it involves a trade-off in performance and it is often followed by a loss of control over one's own content.

– 16 –


5.1.2 Increasing bandwagon pressure and perceived valueHerd behavior, indirect network effect and direct network effect are all bandwagon effects. For the model presented in this paper, the minimum requirement assumed is that the value of an innovation never declines as one more individual adopts. In mathematical terms, this means that the value is increasing as function of the number of adopters. Typical assumptions are that the value is increasing linearly, or that it is increasing exponentially eventually reaching a saturation point. In the saturation point, adding one user does not affect the value anymore significantly.

Chart 2. The increasing value or bandwagon pressure visualized as function of the number of adopters. Herd behavior is usually expected to increase linearly to the number of adopters, whereas other bandwagons increase exponentially to the number of adopters. The exponential increase is limited by the saturation occurring when most potential adopters have adopted. For the model presented in this paper, the minimum assumption is that the pressure and value is increasing, i.e. that it does not decline at any point with an added user.In case of herd behavior, the bandwagon pressure is increasing with the number of users. Herding says nothing about changed utility. For indirect and direct network effects, the theory says that the utility of adoption is increasing with the number of users. For the model to hold, the only requirement is that the pressure does not decline at any point due to an added user. An example of such a situation is telemarketing. Many individuals find calls from telemarketers as disturbing and adding one telemarketer to the network actually makes the total utility of belonging to this network smaller. These situations are treated as exceptions, and are not included in this model.

– 17 –


5.1.3 Technology evolutionMany diffusion studies do not to take into account that the innovation itself is actually evolving during the process of diffusion. Diffusion studies typically involve both time-series and cross-sectional data on markets, and adding technology evolution makes the models more complex. The model easily becomes too complex to be of any use. Nevertheless, technologies do evolve during diffusion and sometimes that very evolution is a significant driver of adoption. This was the case in the hard-drive market between 1977 and 1994 (Christensen 1999).

Chart 3. The increasing utility of an innovation visualized as function of R&D investments. According to Schilling, investments in R&D on an innovation produces an S-shaped curve, whereas Christensen assumed only a linearly increasing utility. For this model, the minimum assumption is that the utility of an innovation is increasing, i.e. that no investment in an innovation makes it less useful. Schilling argued, that the utility of a technology increases in an S-shaped fashion as function of R&D investment (Schilling 2008). Christensen used a linear model where the utility of a technology increased linearly with time (Christensen 1999). For this model, the only constraint is that the utility of an innovation is increasing over time and over R&D investment, i.e. that the utility does not decrease. As the technology evolves, prices might also change. Sometimes this evolution produces higher value products with higher prices. Individuals' assessments of the technology should change to reflect this evolution. In such cases, the adopter distribution is shifted right; people on average have higher valuations of the technology.

5.1.4 Price evolutionThe price of an innovation typically decreases over time. The price evolution is strongly

– 18 –


related to technology evolution and to diffusion itself, as scale economies typically kick in at some point.

Chart 4. Price evolution. The price of an innovation typically decreases over time. This can be due to improved production processes and to scale economies.Even though the price evolution is highly dependent on technology evolution, it is also a result of scale economies. The price evolution is presented here in order to catch the effect of scale economies and to describe the effect it has on technology diffusion. Basically scale economies causes the unit cost to decline with increasing number of units produced. In a competitive market this results in declining price with increasing number of units produced as visualized in chart 4. The decrease in price is also often the result of a pricing strategy.

5.2 The S-curveThe point with all the previous charts (chart 1-4) is that they all contribute to the S-shaped diffusion curve (chart 5, below). Only one force is needed, assuming that individual adoption thresholds can be modeled as at least a uniform distribution, to end up with the S-shaped diffusion curve. In the three consecutive sections, partial models are presented to illustrate the functioning of the complete model.

5.2.1 Adopter threshold distribution and bandwagonsThe adopter threshold is a valuation point that need to be exceeded to cause adoption. Once the threshold is exceeded, the individual adopts. Starting from the minimum assumptions, by combining the uniform adoption threshold distribution (dotted line in

– 19 –


chart 1) with the increasing bandwagon pressure or utility (chart 2), and integrating from time 1 to time 2 produces an exponentially increasing adoption curve. Each new adoption has to add enough pressure or value for exceeding the threshold of the next adopter – otherwise diffusion will stop. The curve will eventually stop increasing due to market saturation – if diffusion is not stopped before that – but saturation is out of scope for this paper. Alternatively, the less strict assumptions are a bell-shaped adopter threshold distribution and/or an exponentially increasing bandwagon pressure/value. Integration from time 1 to time 2 also in these cases produces an S-curve – but this time the increase is even steeper.

5.2.2 Adopter threshold distribution and technology evolutionTo combine the adopter threshold distribution with technology evolution, one substitution is needed. The technology evolution is now represented as function of R&D investment in chart 3. R&D investments need to be substituted by time. Assuming that R&D investments cannot be undone and that no invested sum makes the technology worse, the technology evolution function over investment translates into technology evolution over time. This produces a utility of technology increasing over time. Integrating from time 1 to time 2 then produces an S-curve in a similar fashion as in the previous section.

5.2.3 Adopter threshold distribution and price evolutionCombining the adopter threshold distribution with price evolution again produces an S-shaped diffusion curve. A substitution is needed to arrive at the result. The number of units produced in the price evolution curve (chart 4) need to be substituted by time. Assuming that there is competitive pressure on price, the decline in unit cost will translate into declining price over time. The minimum assumptions are again sufficient: adopters' threshold valuations of the technological innovation are uniformly distributed and the price is declining over time. Integrating from time 1 to time 2 again produces an S-shaped diffusion curve.

5.2.4 The complete modelFor this model to have any validity, it should have verification in empiric research. Taking all these forces into account and integrating from time 1 to time 2 produces an S-shaped diffusion curve. Only this time the curve is increasing at a steeper rate than in any of the earlier partial models. All these forces thus reinforce the effect on diffusion.

– 20 –


Chart 5. Plotting the number of adopters of an innovation over time usually produces an S-shaped curve. The S-curve has been verified in many empiric diffusion studies. When talking about the S-curve, there are some parts which are referred to by specific terms. In the beginning when the diffusion “takes off” there is a tipping point (point 1 in chart 5). It is not clear what the market penetration is at the tipping point: Rogers says that it is between 3 % and 16 % (Rogers 2003); the Bass-model depicts that it is when the impact of bandwagon effects are equal to the impact of marketing (Phillips 2006); and Golder & Tellis found that the average market penetration was 1,7% at the tipping point for electrical household appliances (Golder & Tellis 1997). Golder & Tellis further proposed a predictor for tipping based on previous years' increase in sales and installed base. The tipping point is also the point that distinguishes early markets from non-early markets.The second interesting point in the S-curve is the inflection point. This is the point where the second derivative of the function turns negative, i.e. where the acceleration of the diffusion is negative. The third point of importance in the graph 5 is the saturation point. At this point the markets are characterized by other forces than the bandwagon forces described in this document. At the saturation point the competitive landscape in the industry is very different from the earlier phases. Here the competitors compete for market share and usually their effort to increase profits are focused on the internal efficiency of their companies.It is important to understand that not all technological innovations diffuse. The change in all these forces should increase today so that the technology will continue to diffuse tomorrow. The sum of all these forces should change enough to make the following adopters adopt. The model is simple in the sense that it only assumes that individuals adopt once they perceive that the value of an innovation exceeds their threshold valuation. If this change is not enough to exceed the thresholds of the following adopters,

– 21 –


the diffusion will stop.The bandwagon model presented can also be approximated by the differential equationx ' t =c∗x t ∗[A−x t ]

where x(t) is the number of adopters at time t, x'(t) is the speed of adoption at time t, A is the number of potential adopters and c is some coefficient. The model thus says that the speed of adoption (number of adopters in a short time period) is dependent on the influence of those that already have adopted ( c * x(t) ) on those that have not yet adopted ( A – x(t) ). The equation should not be taken literally, e.g. bandwagon effects could vary over time. One implication that is well illustrated in this model is that there are only two stable adopter groups – zero adopters, and all adopters (A). This illustrates the difficulty of starting a bandwagon and the proneness to lock-in seen in ICT markets. The model also indicates that bandwagon markets do not support several standards, but there are some examples of bandwagon markets where several standards co-exist, e.g. the video gaming market.Some technologies fail to diffuse. If there was not a need for the technology in the first place, it is reflected in this model as the adopters' valuation being lower than its price. Some researchers talk about decision points as situations where individuals are forced to make a choice. The effect of added users is thus not immediate as some individuals revise their valuation of the innovation first when they face a decision point. Other researchers talk about cycles (time period) where the increase in forces in one cycle have to exceed some threshold to make individuals adopt in the next cycle. This notion is a rough simplification of the diffusion process as it takes away the fact that diffusion is a continuous process seen from the macro level.Stop of diffusion can also be caused by substitution of a superior innovation, but this is left out of scope in this paper.The tricky part with this mixed model is to distinguish which of these forces are dominant in the diffusion of a specific technology. The research on technology diffusion is rather conclusive on the point that the demand is not only dependent on the price, but also on installed base and similar. Thus the notion in political economy that demand is a function of price solely is inaccurate in ICT markets. In order to look for some practical prescriptive implications and contexts where bandwagon effects are present, findings of empiric research is covered in the following section.

6 Empirical findings on the impact of bandwagonsMost research relating to bandwagon effects have been based on mathematical modeling and simulation. These theoretical works are often combined with case based or anecdotal evidence and do not consequently provide particularly conclusive evidence. Although these provide insight to the structure of bandwagons, they do not provide an order of magnitude of their impact. The context is lacking. That context could be provided by

– 22 –


empirical research, but in the case of bandwagon effects the number of empirical studies is small. The econometric studies all develop rather complex regression models with significant differences making the results difficult to compare. The findings of econometric works based on empirical evidence known to the author are listed below in table 1 together with some other works in the field with indicative results.

Table 1. Findings and indicative results about bandwagon effects. Many of the models developed are exponential, thus illustrating the findings with percentages causes an information loss. This is nevertheless done to improve the intuitive understanding of the findings and to simplify comparison. The comparisons made assume that all other variables except for the ones mentioned are equal.No Case Type of

effectImpact Comments and references

1 Spreadsheet 1989-1991(1987-1992)

Direct network effect, indirect network effect

LOTUS' 64.65% market share (installed base) commanded a 62% price premium compared to a new entrant.

Gandal found that customers were between 1989 and 1991 willing to pay a significant premium for LOTUS-spreadsheet compatibility similar to the premium associated with a significant feature (e.g. filesharing over LAN) thus showing a direct network effect (Gandal 1994).Brynjolfsson & Kemerer had a longer time-series of data and found that the network effect for LOTUS was more significantly related to the installed base of LOTUS than the installed base of all LOTUS and compatible products indicating that the compatible products (excl. LOTUS) were seen as only relatively close substitutes (Brynjolfsson & Kemerer 1996).

2 Database – Spreadsheet 1989-1991

Indirect network effect

LOTUS compatibility commanded a 36% price premium.

Gandal found a correlation between LOTUS compatibility and price premium for database software indicating an indirect network effect (Gandal 1995).

3 Database 1989-1991

None No measurable effect

Gandal found no network effect for database software, i.e. database compatibility features did not correlate to price premiums (Gandal 1995).

– 23 –


No Case Type of effect

Impact Comments and references

4 Word processing software1987-1991

Direct network effect, indirect network effect

1% difference in market share commanded a 4% price premium.

Chakravarty & al. found strong network effects surrounding the word processing software market. The largest provider with a 45,7% market share was able to command a 3.6-7.2 fold price premium compared to a new entrant ceteris paribus (Chakravarty et al. 2006).

5 Web server software1995-1997


1% difference in market share commanded a 19% price premium.

Gallaugher & Wang found that the web server software market demonstrated significant network effect. They found that 11,48% market share commanded a seven-fold price premium compared to a new entrant ceteris paribus (Gallaugher & Wang 2002). They also found that significant features commanded price premiums between 27% (remote admin) and 164% (GUI interface).

6 ATM's 1971-1979


Adding one branch to an average bank increased the hazard rate (probability of adoption) by 5.4-10.2%

Saloner and Shepard found that banks with larger expected networks found it profitable to adopt ATMs earlier than banks with smaller expected networks (Saloner & Shepard 1995).

7 Automated Clearing House (ACH) 1995-1997

Direct network effect

If the probability of adoption for a bank was 50% during a specified time frame, this probability would increase by 4.7 percentage points if one more bank adopted.

Gowrisankaran & Stavins found that the diffusion of ACH-services (automated clearing house) was affected by network effect (Gowrisankaran & Stavins 2004).

8 Video gaming market


Market able to support several standards.

Katz & Shapiro predicted in 1985 that the video gaming industry would benefit from indirect network effect

– 24 –




(Katz & Shapiro 1985). The interesting thing is that the market supports several standards (providers); the market is thus not locked in (Schilling 2008).

9 CD-players1987-1996


Market was for a long time locked into one standard.

Van den Bulte found that CD-players did not diffuse faster than other electrical household appliances indicating a lack of network effect (Bulte 2000). The fact that the music market was locked into one standard for a long time suggests that indirect network effect had at least some impact on the market.

10 Black-and-white television 1949-1964


- Van den Bulte found that televisions, radios, cellphones, VCR's, and PCs diffused faster than other electrical household appliances (Bulte 2000). He attributed this to the large initial investments made in networks, but this does not explain the diffusion speed of the VCR. The large sunk investments probably play a significant role for most of these, but these innovations also experience network effect. At least part of the diffusion speed should be contributed to network effect.

11 Color television 1965-1979


-

12 Radio1924-1932


-

13 Home PC1983-1996


-

14 Cellular telephone1990-1996

Direct network effect

-

15 VCR's: VHS vs. Betamax1981-1988


VHS became the de facto standard in the VCR market.

Park explains the victory of VHS in the format battle against Betamax by indirect network effect. He found that the indirect network effect measured by installed base explains 70.3% to 86.8% of the log relative sales (Park 2004). Van den Bulte found that VCR's did

– 25 –




diffuse faster than other electrical household appliances (Bulte 2000).

16 Voting Herding 6 % of voters changed opinion.

Mehrabian found in an experiment that 6% of the participants changed their votes in favor of the leading candidate after having seen poll results. (Mehrabian 1998)

17 Browser technology: The browser war1994-1998


Microsoft took legally questionable action.

This is not so much proof of the existence of network effect, as it is proof of belief in network effect. Microsoft was found to have misused monopoly power to gain market share in the browser war. (Bresnahan 2001)

Bandwagon effects are often vaguely described and illustrated only with anecdotal evidence. It is often implied that bandwagon effects are somehow unmeasurable or too complex to understand. The findings in table 1 are collected to present what has been found about bandwagon effects. Most bandwagon effects are exponential in nature, thus estimating diffusion ex-ante will always involve large error margins. These findings show that even though the error margins in estimation will remain large, there is an upper and lower boundary. These findings also show that bandwagon effects are measurable ex-post.In addition to the explicit findings listed in table 1, there are also some implicit findings which are discussed below.

6.1.1 Spreadsheet marketsEven though the spreadsheet market was saturated in the beginning of the 1990's, the computer market grew exponentially and opened up new customer segments for spreadsheets. Gandal compared price premiums with LOTUS-compatibility, thus assuming that LOTUS had reached a de facto standard position in the market. The results indicate that this was the case. A rapidly growing number of computer users opened up a whole new unserved market segment, which might be one of the reasons to the later switch in market leadership.

6.1.2 Database marketsPrice premium in database software was correlated with compatibility with spreadsheet software, but not correlated with database formats. This could be interpreted as users not expecting to migrate data from one database to another, or that no database format had

– 26 –


achieved de facto standard thus making a good estimator of installed base and consequently network effect. The finding discredits the theories stating that knowledge about a standard forms an informational bandwagon with a product supporting the standard. At least in database markets between 1989 and 1991, such an effect was not present.

6.1.3 VCR's, CD's and video gamesNeither the CD nor the VCR market supported several standards, which implies that both are affected by indirect network effect. The VCR diffused faster than other household electrical appliances, which the CD did not, and the victory of the VHS-format over the Betamax format is largely explained by indirect network effect (Park 2004). At the time studied, the consumption of videos differed from music in the sense that videos were rented and returned to video rentals, but CD's were bought and not returned. Still, CD's needed distribution channels, thus this does not satisfactorily explain the difference in diffusion speeds. One plausible explanation is that the VCR market was actually new whereas the music market existed long before CD's; the VCR's were competing against non-consumption whereas the CD's were replacing outdated music hardware, thus allowing the VCR's to diffuse faster.Huge efforts were spent on the introduction of music CD's at the same time with the introduction of CD-players (Rohlfs 2001). The introduction was successful and it is interesting to note that later music formats have been unable to break the lock-in related to CD's (e.g. DAT and minidisc) until the introduction of MP3's. MP3's were apparently disruptive enough and provided enough stand-alone value to break the CD-format lock-in. Comparing video games to VCR's and CD's, there is one interesting difference: the video gaming market has always supported several standards (today e.g. Sony PlayStation, Microsoft X-Box, Nintendo Wii) whereas the VCR and CD markets have not (Schilling 2008). Schilling argues that the tastes for video games differ to the extent that users find adopting different standards valuable. This is an example of a case where the impact of consumer taste differences are the same order of magnitude as the impact of indirect network effect.

6.2 Implications for the model presentedHerding has not been extensively covered in this paper. The effect itself is difficult to separate from other bandwagon effects. In order to estimate a bandwagon effect, a delicate market situation is needed which allows for quantitative estimation of an upper and lower boundary. In addition, there needs to be qualitative evidence suggesting that one specific bandwagon effect is more significant than other in a case. Otherwise only the totality of bandwagon effects can be estimated.Herding has large implications e.g. in elections, and leaving it completely out in technology diffusion models seems ignorant. Herding caused 6% of the respondents in an experiment to change opinion for the leader; that is 6 percentage points more for the

– 27 –


leader, and 6 percentage points less for the other. In markets that are prone to tipping, disregarding herding might be a fatal mistake as there is not necessarily a second prize. Unfortunately the impact magnitude of herding in ICT diffusion is not illustrated by any case examples.For informational bandwagons, the findings are twofold. There is no empirical evidence on how the spread of new information affects diffusion of ICT. Lack of information certainly prevents adoption and diffusion, thus information can be treated as a prerequisite for – but not as a driver of – adoption. No evidence was found supporting the notion that knowledge as a complement would form an informational bandwagon with technology. The case with database and spreadsheet software illustrates this point. For spreadsheet software, a bandwagon was identified, but for database software there was none. Further, database software premiums were correlated with spreadsheet software compatibility, indicating a bandwagon between these two. These findings indicate that the bandwagon was caused by spreadsheets used as a means of communication. The bandwagon is thus not an informational bandwagon, but an example of network effect. There are several examples of innovations with indirect network effects, thus evidence for its existence is rather conclusive. The impact of indirect network effect varied from 36% price premium in the LOTUS-database case up to a seven-fold price premium for the largest player in the web server software market. Indirect network effect also caused innovations to diffuse faster and caused lock-in in the CD and VCR markets. The example with the video gaming market illustrates a case where the impact of differences in consumer preferences are similar order of magnitude as the impact of indirect network effect. The existence of direct network effect is seldom treated in empirical research papers; it is taken for granted. Direct network effect is seen in communication networks, and some software products become part of a communication tool. This is the case with spreadsheet and word processing software. As both have significant stand-alone value and are not communication tools as such, they do not generalize well for communication tools in general.The model explains diffusion of ICT in many cases and has some prescriptive implications. The empirical findings can be used as guideline for determining what kind of impact can be expected in different contexts. The model is thus useful for understanding and estimating diffusion of technologies.

7 ConclusionsThe contributions of this paper are a few. This paper is an attempt at clarifying the vocabulary to be used in the context of bandwagon effects. This paper presents a mixed model of influence for technology diffusion and explains how these different forces

– 28 –


interact to produce the S-curve. This paper also gathers all econometric studies in the field to convey an understanding of the magnitude of impact these forces have, as well as illustrating in what context they can be expected. In table 2, the findings about bandwagon effects are summarized.

Table 2. Qualitative findings and implications for prescriptive purposes.Bandwagon effect FindingsHerding Probably affects ICT diffusionInformational bandwagon

There is no empirical evidence supporting informational bandwagons in ICT markets, but information should be treated as a prerequisite for other forces to affect the potential adopters.

Indirect network effect Conclusive empirical evidence was found supporting the indirect network effect theories. The impact varies between 36% price premium to seven-fold price premium; the effect has also caused lock-in and de facto standardization.

Direct network effect The existence of direct network effect is taken for granted in theory. Examples of up to 7-fold price premiums due to installed base were found, but these cases also experienced indirect network effect.

The contexts where technologies evolve are complex and as there are multiple forces at play simultaneously, it is difficult to estimate the impact of separate forces. People as consumers are complex and they work in a reactive way (Akerlof & Shiller 2009; Soros 2008); they form their decision models continuously taking into account what they hear and what they learn. Modeling such behavior will always include uncertainties and as most forces covered in this paper are exponential in their nature, the results are very rough. But even though the results are very rough, they are still there and they talk for themselves. The empirical research results available on bandwagon effects strongly supports the claim that bandwagon effects are significant drivers of information and communication technology diffusion. There is not a clear ranking order of bandwagon effects, but there seems to be a belief that the impact of direct network effect is order of magnitude stronger than the impact of other bandwagon effects. There is no quantitative evidence neither supporting nor discrediting this belief. In some cases the distinction between the two are blurred. What is clear, though, is that innovations have both inherent and extrinsic value; that part of the value lies in the innovation itself and part in the network the innovation belongs to. The value of an innovation is composed of several parts, and these parts varies from innovation to innovation. Bandwagon effects are often mentioned as one of the few identified sources of sustainable

– 29 –


competitive advantage, bandwagon effects thus play a dominant role in ICT markets. Unfortunately for companies, the bandwagon effects are often outside their direct control. Other factors affecting diffusion were also covered in this paper, and these are usually under the direct influence of the producers. If the bandwagon has not taken off yet, these other factors are what can be affected. A company can decide to invest more in R&D to enable technology evolution and improve stand-alone value. The pricing decisions can be revised. A marketing campaign can be launched. These are all under the influence of the producers.For a company in leading position in a bandwagon market, the findings can be used as a measure of competitive advantage. A company can decide to save in R&D expenditures and copy solutions of competitors. The installed base advantage should allow a company to lag in feature development and still maintain its market position.For policymakers, the findings in this paper help understanding the consequences of policy decisions in bandwagon markets. Free market economy suggests that the markets should be left without regulation, but unfortunately bandwagon markets have a tendency towards lock-in – a type of market failure.

7.1 Future researchThere is a clear reason to the small number of econometric studies in this area; it requires vast amount of data. Focusing on purely theoretical models is likely to produce more results with less effort, explaining the imbalance between theoretic and empirical research.Many scholars conclude their papers with a call for more empiric research in the field. That is certainly welcome, but in addition, the focus of work could be shifted from proving the existence of bandwagon effects to understanding their impact. This would enable more accurate prescriptive models. One possible way of doing this is to use the regression models created in the econometric studies covered in this paper to run different scenario simulations. This would show how the models relate to each other in the different phases of diffusion. Now, the results are presented as lists of regression coefficients together with a formula which is accurate but unintuitive to understand. The results of such a simulation could further be used to compare the impact magnitude of bandwagon effects with marketing and similar activities taken to accelerate diffusion.Another interesting direction for further research could be to use this information to understand how new entrants can end a lock-in to enter a market.

7.2 AcknowledgmentsI am grateful to Torulf Jernström for bearing with endless conversations on the topic and for providing valuable opinions.

– 30 –


ReferencesAbrahamson, E. & Rosenkopf, L., 1993. Institutional and Competitive Bandwagons:

Using Mathematical Modeling as a Tool to Explore Innovation Diffusion. The Academy of Management Review, 18(3), 487-517.

Abrahamson, E. & Rosenkopf, L., 1997. Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation. Organization Science, 8(3), 289-309.

Akerlof, G.A. & Shiller, R.J., 2009. Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism, Princeton University Press.

Banerjee, A.V., 1992. A Simple Model of Herd Behavior. Quarterly Journal of Economics, 107(3), 797-817.

Bass, F., 1963. A Dynamic Model of Market Share and Sales Behavior. In Winter Conference American Marketing Association. Chicago, p. 269. Available at: http://www.bassbasement.org/F/N/FMB/Pubs/Bass 1963 Dynamic Model.pdf.

Bresnahan, T.F., Stem, S. & Trajtenberg, M., 1997. Market segmentation and the sources of rents from innovation: personal computers in the late 1980s. Rand Journal of Economics, 28.

Bresnahan, T.F., 2003. The economics of the Microsoft case, Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=304701.

Bresnahan, T., 2001. Network effects and Microsoft. Stanford Institute for Economic Policy Research Discussion Paper No. 00-51. Available at: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Network+Effects+and+Microsoft#0.

Bryce, R. & Gross, N.C., 1943. The Diffusion of Hybrid Seed Corn in Two Iowa Communities. Rural Sociology, (8), 15-24.

Brynjolfsson, E. & Kemerer, C.F., 1996. Network Externalities in Microcomputer Software: An Econometric Analysis of the Spreadsheet Market. Management Science, 42(12), 1627-1647.

Bulte, C.V., 2000. New Product Diffusion Acceleration: Measurement and Analysis. Marketing Science, 19(4), 366-380.

Campbell-Kelly, M., 2004. From Airline Reservations to Sonic the Hedgehog, MIT Press. Available at: http://mitpress.mit.edu/0262033038.

Chakravarty, S., Dogan, K. & Tomlinson, N., 2006. A hedonic study of network effects in the market for word processing software. Decision Support Systems, 41(4), 747-763. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0167923604002349.

Christensen, C. & Raynor, M., 2003. The innovators solution, Harvard Business School. Available at: http://www.vedpuriswar.org/book_review/the_innovator's solution.PDF.

– 31 –


Christensen, C., Anthony, S. & Roth, E., 2004. Seeing what Is next, Available at: http://www.vedpuriswar.org/book_review/Seeing What is Nex_.pdf.

Christensen, C., 1999. The Innovator's Dilemma, Available at: http://www.executiveforum.net/pdfs/christensen.pdf.

Dhanaraj, C. & Parkhe, A., 2006. Orchestrating innovation networks. Academy of Management Review, 31(3), 659-669.

Farrell, J. & Saloner, G., 1992. Converters, compatibility, and the control of interfaces. The Journal of Industrial Economics, 40(1), 9-35. Available at: http://www.jstor.org/stable/2950625.

Farrell, J. & Saloner, G., 1988. Coordination through committees and markets. The RAND Journal of Economics, 19(2), 235-252. Available at: http://www.jstor.org/stable/2555702.

Farrell, J. & Saloner, G., 1966. Installed Base and Compatibility: Innovation, Product Preannouncements, and Predation. Science (New York, N.Y.), 151(3712), 867-868. Available at: http://www.ncbi.nlm.nih.gov/pubmed/9539999.

Farrell, J. & Saloner, G., 1985. Standardization, Compatibility and Innovation. Rand Journal of Economics, 16(1), 70-83.

Fisher, F.M., McGowan, J.J. & Greenwood, J.E., 1983. Folded, Spindled and Mutilated, MIT Press. Available at: http://mitpress.mit.edu/catalog/item/default.asp?tid=9480&ttype=2.

Gallaugher, J. & Wang, Y., 2002. Understanding network effects in software markets: Evidence from Web server pricing. MIS Quarterly, 26(4), 303–327. Available at: http://www.jstor.org/stable/4132311.

Gandal, N., 1995. Competing compatibility standards and network externalities in the PC software market. The Review of Economics and Statistics, 77(4), 599–608. Available at: http://www.jstor.org/stable/2109809.

Gandal, N., 1994. Hedonic price indexes for spreadsheets and an empirical test for network externalities. the RAND Journal of Economics, 25(1), 160–170. Available at: http://www.jstor.org/stable/2555859.

Geroski, P.A., 2000. Models of technology diffusion. Research Policy, 29, 603-625.Gladwell, M., 2000. The Tipping Point, Abacus.Golder, P.N. & Tellis, G.J., 1997. Will It Ever Fly? Modeling the Takeoff of Really

New Consumer Durables. Marketing Science, 16(3), 256-270.Gowrisankaran, G. & Stavins, J., 2004. Network externalities and technology

adoption: lessons from electronic payments. RAND Journal of Economics, 35(2), 260–276. Available at: http://www.jstor.org/stable/1593691.

Herpen, E.V., Pieters, R. & Zeelenberg, M., 2005. How Product Scarcity Impacts on

– 32 –


Choice: Snob and Bandwagon Effects. Advances in Consumer Research, 32, 623-625.Katz, M. & Shapiro, C., 1986. How to license intangible property. The Quarterly

Journal of Economics, 101(3), 567-589. Available at: http://www.jstor.org/stable/1885697.Katz, M. & Shapiro, C., 1992. Product introduction with network externalities. The

Journal of Industrial Economics, 40(1), 55-83. Available at: http://www.jstor.org/stable/2950627.

Katz, M. & Shapiro, C., 1994. Systems competition and network effects. The Journal of Economic Perspectives, 8(2), 93–115. Available at: http://www.jstor.org/stable/2138538.

Katz, M. & Shapiro, C., 1986. Technology adoption in the presence of network externalities. The Journal of Political Economy, 94(4), 822-841. Available at: http://www.journals.uchicago.edu/doi/abs/10.1086/261409.

Katz, M.L. & Shapiro, C., 1985. Network Externalities, Competition, and Compatibility. American Economic Review, 75(3), 424-440.

Leibenstein, H., 1950. Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand. Quarterly Journal of Economics, 64(2), 183-207.

Mehrabian, A., 1998. Effects of Poll Reports on Voter Preferences. Journal of Applied Social Psychology, 23(December), 2119-2130.

Moore, G., 1991. Crossing the chasm, Available at: http://www.cecid.org.hk/downloads/pastevents/CUHK_Component_Based.pdf.

Moore, G., 1995. Inside the tornado, Available at: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Inside+the+Tornado#1.

Park, S., 2004. Quantitative analysis of network externalities in competing technologies: The VCR case. Review of Economics and Statistics, 86(4), 937–945. Available at: http://www.mitpressjournals.org/doi/abs/10.1162/0034653043125275.

Parolini, C., 1999. The Value Net, Available at: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:The+Value+Net#5.

Phillips, F., 2006. On S-curves and tipping points. Technological Forecasting and Social Change, 74(6), 715-730.

Rogers, E.M., 2003. Diffusion of Innovations Fifth Edit. B. Wallace, New York: The Free Press.

Rohlfs, J., 1974. A Theory of Interdependent Demand for a Communications Service. Bell Journal of Economics and Management Science, 5(1), 16-37.

Rohlfs, J., 2001. Bandwagon Effects in High-Technology Industries, The MIT Press.Saloner, G. & Shepard, A., 1995. Adoption of Technologies with Network Effects:

An Empirical Examination of the Adoption of Automated Teller Machines. Rand Journal of

– 33 –


Economics, 26(3), 479-501.Scharfstein, D.S. & Stein, J.C., 1990. Herd Behavior and Investment. American

Economic Review, 80(3), 465-479.Schilling, M.A., 2008. Strategic Management of Technological Innovation Second

Edi., McGraw - Hill.Schumpeter, J.A., 1934. The Theory of Economic Development, Transaction

Publishers.Shy, O., 2001. The Economics of Network Industries, Cambridge University Press.Soros, G., 2008. The New Paradigm for Financial Markets, Public Affairs.Takeyama, L.N., 1994. The Welfare Implications of Unauthorized Reproduction of

Intellectual Property in the Presence of Demand Network Externalities. The Journal of Industrial Economics, 42(2), 155-166.

Valente, T.W., 2005. Network Models and Methods for Studying the Diffusion of Innovations,

Valente, T.W., 1996. Social network thresholds in the diffusion of innovations. Social Networks, 8733(95).

Wejnert, B., 2002. Integrating Models of Diffusion of Innovations: A Conceptual Framework. Annual Review of Sociology, 28(2002), 297-326.

– 34 –

bandwagon effects in ict diffusion

Documents