Diffusion of Innovations

The diffusion of innovations, also referred to as diffusion theory, was developed by Everett Rogers (1962; 2003). This theory tries to explain how an innovation is communicated over time through different channels to members of a social system. There are four main aspects of this approach.

First, there is the innovation. In the case of communication technologies, the innovation is some technology that is perceived as new. Rogers also defines characteristics of innovations: relative advantage, compatibility, complexity, trialability, and observability.

So, if someone is deciding to purchase a new mobile phone, characteristics would include the relative advantage over other mobile phones; whether or not the mobile phone is compatible with the existing needs of the user; how complex it is to use; whether or not the potential user can try it out; and whether or not the potential user can see others using the new mobile phone with successful results.

Information about an innovation is communicated through different channels. Mass media is good for awareness knowledge. For example, each new iPhone has Web content, television commercials, and print advertising announcing its existence and its features.

Interpersonal channels are also an important means of communication about innovations. These interactions generally involve subjective evaluations of the innovation. For example, a person might ask some friends how they like their new iPhones.

Rogers (2003) outlines the decision-making process a potential user goes through before adopting an innovation. This is a five-step process.

The first step is knowledge. You find out there is a new mobile phone available and learn about its new features. The next step is persuasion—the formation of a positive attitude about the innovation. The third step is when you decide to accept or reject the innovation. Implementation is the fourth step. Finally, confirmation occurs when you decide that you made the correct decision.

Another stage discussed by Rogers (2003) and others is “reinvention,” the process by which a person who adopts a technology begins to use it for purposes other than those intended by the original inventor. For example, mobile phones were initially designed for calling other people regardless of location, but users have found ways to use them for a wide variety of applications ranging from alarm clocks to personal calendars and flashlights.

Have you ever noticed that some people are the first to have the new technology gadget, while others refuse to adopt a proven successful technology? Adopters can be categorized into different groups according to how soon or late they adopt an innovation.

The first to adopt are the innovators. Innovators are special because they are willing to take a risk adopting something new that may fail. Next come the early adopters, the early majority, and then the late majority, followed by the last category, the laggards. In terms of percentages, innovators make up the first 2.5% percent of adopters, early adopters are the next 13.5%, early majority follows with 34%, late majority are the next 34%, and laggards are the last 16%.

Adopters can also be described in terms of ideal types. Innovators are venturesome. These are people who like to take risks and can deal with failure. Early adopters are respectable. They are valued opinion leaders in the community and role models for others. Early majority adopters are deliberate. They adopt just before the average person and are an important link between the innovators, early adopters, and everyone else. The late majority are skeptical. They are hesitant to adopt innovations and often adopt because they pressured. Laggards are the last to adopt and often are isolated with no opinion leadership. They are suspicious and resistant to change. Other factors that affect adoption include education, social status, social mobility, finances, and willingness to use credit (Rogers, 2003).

Adoption of an innovation does not usually occur all at once; it happens over time. This is called the rate of adoption. The rate of adoption generally follows an S-shaped “diffusion curve” where the X-axis is time and the Y-axis is percent of adopters. You can note the different adopter categories along the diffusion curve.

Figure 3.1 shows a diffusion curve. See how the innovators are at the very beginning of the curve, and the laggards are at the end. The steepness of the curve depends on how quickly an innovation is adopted. For example, DVD has a steeper curve than VCR because DVD players were adopted at a faster rate than VCRs.

Also, different types of decision processes lead to faster adoption. Voluntary adoption is slower than collective decisions, which, in turn, are slower than authority decisions. For example, a company may let its workers decide whether to use a new software package, the employees may agree collectively to use that software, or finally, the management may decide that everyone at the company is going to use the software. In most cases, voluntary adoption would take the longest, and a management dictate would result in the swiftest adoption.

Source: Technology Futures, Inc.

Moore (2001) further explored diffusion of innovations and high-tech marketing in Crossing the Chasm. He noted there are gaps between the innovators and the early adopters, the early adopters and the early majority, and the early majority and late majority.

For a technology’s adoption to move from innovators to the early adopters the technology must show a major new benefit. Innovators are visionaries that take the risk of adopting something new such as virtual home assistants.

Early adopters then must see the new benefit of virtual home assistants before adopting. The chasm between early adopters and early majority is the greatest of these gaps. Early adopters are still visionary and want to be change agents. They don’t mind dealing with the troubles and glitches that come along with a new technology. Early adopters were likely to use a beta version of a new service or product.

The early majority, on the other hand, are pragmatists and want to see some improvement in productivity—something tangible. Moving from serving the visionaries to serving the pragmatists is difficult; hence Moore’s description of “crossing the chasm.”

Finally, there is a smaller gap between the early majority and the late majority. Unlike the early majority, the late majority reacts to the technical demands on the users. The early majority is more comfortable working with technology. So, the early majority would be comfortable using a virtual home assistant like the Amazon Echo but the late majority is put off by the perceived technical demands. The technology must alleviate this concern before late majority adoption.

Another perspective on adoption can be found in most marketing textbooks (e.g., Kottler & Keller, 2011): the product lifecycle. As illustrated in Figure 3.2, the product lifecycle extends the diffusion curve to include the maturity and decline of the technology. This perspective provides a more complete picture of a technology because it focuses our attention beyond the initial use of the technology to the time that the technology is in regular use, and ultimately, disappears from market. Remember laserdisc or Myspace?

Considering the short lifespan of many communication technologies, it may be just as useful to study the entire lifespan of a technology rather than just the process of adoption.

Source: Technology Futures, Inc.

Other Theories of Adoption

Other theorists have attempted to explain the process of adoption as well. Among the most notable perspectives in this regard are the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology. These models emerged from the need to identify factors that can help predict future adoption of a new technology when there is no history of adoption or use of the technology.

The Theory of Planned Behavior (TPB) (Ajzen, 1991; Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975) presumes that a suitable predictor of future behavior is “behavioral intention,” a cognitive rather than behavioral variable that represents an individual’s plans for adopting (or not adopting!) an innovation. Behavioral intentions are, in turn, predicted by attitudes toward the innovation and the innovators.

The Technology Acceptance Model (Davis, 1986; Davis, Bagozzi & Warshaw, 1989) elaborates on TPB by adding factors that may predict attitudes toward an innovation and behavioral intentions, including perceived usefulness, perceived ease of use, and external variables.

The Unified Theory of Acceptance and Use of Technology attempts to combine elements of several theories of technology adoptions including the Theory of Planned Behavior, the Technology Acceptance model, and Diffusions of Innovations (Venkatesh, Morris, Davis; & Davis (2003). The authors argue that there are four core determinates of user acceptance and behavior. These are performance expectancy, effort expectancy, social influence, and facilitating conditions.

A substantial body of research has demonstrated the efficacy of these factors in predicting behavioral intentions (e.g., Jiang, Hsu & Klein, 2000; Chau & Hu, 2002, but much more research is needed regarding the link between behavioral intentions and actual adoption at a later point in time.

Another theory that expands upon Rogers’ diffusion theory is presented in Chapter 4. Grant’s pre-diffusion theory identifies organizational functions that must be served before any consumer adoption of the technology can take place.

Critical Mass Theory

Have you ever wondered who had the first email address or the first telephone? Who did they communicate with? Interactive technologies such as telephony, social networking and email become more and more useful as more people adopt these technologies. There have to be some innovators and early adopters who are willing to take the risk to try a new interactive technology.

These users are the “critical mass,” a small segment of the population that chooses to make big contributions to the public good (Markus, 1987). In general terms, any social process involving actions by individuals that benefit others is known as “collective action.” In this case, the technologies become more useful if everyone in the system is using the technology, a goal known as “universal access.”

Ultimately, universal access means that you can reach anyone through some communication technology. For example, in the United States, the landline phone system reaches almost everywhere, and everyone benefits from this technology, although a small segment of the population initially chose to adopt the telephone to get the ball rolling. There is a stage in the diffusion process that an interactive medium has to reach in order for adoption to take off. This is the critical mass. Interestingly, 49% of homes in the US are mobile phone only but the important factor to remember is that even early mobile phone users could call anyone on a land line so the technology didn’t have the same critical hurdle to overcome (CTIA, 2018).

Another conceptualization of critical mass theory is the “tipping point” (Gladwell, 2002). Here is an example. The videophone never took off, in part, because it never reached critical mass. The videophone was not really any better than a regular phone unless the person you were calling also had a videophone. If there were not enough people you knew who had videophones, then you might not adopt it because it was not worth it.

On the other hand, if most of your regular contacts had videophones, then that critical mass of users might drive you to adopt the videophone. Critical mass is an important aspect to consider for the adoption of any interactive technology. Think of the iPhone’s Facetime App. The only people who can use Facetime are iPhone users. Skype, on the other hand, works on all mobile operating systems so it doesn’t have the same limitations as Facetime.

Another good example is facsimile or fax technology. The first method of sending images over wires was invented in the ’40s—the 1840s—by Alexander Bain, who proposed using a system of electrical pendulums to send images over wires (Robinson, 1986). Within a few decades, the technology was adopted by the newspaper industry to send photos over wires, but the technology was limited to a small number of news organizations.

The development of technical standards in the 1960s brought the fax machine to corporate America, which generally ignored the technology because few businesses knew of another business that had a fax machine.

Adoption of the fax took place two machines at a time, with those two usually being purchased to communicate with each other, but rarely used to communicate with additional receivers. By the 1980s, enough businesses had fax machines that could communicate with each other that many businesses started buying fax machines one at a time.

As soon as the critical mass point was reached, fax machine adoption increased to the point that it became referred to as the first technology adopted out of fear of embarrassment that someone would ask, “What’s your fax number?” (Wathne & Leos, 1993). In less than two years, the fax machine became a business necessity.

Social Information Processing

Another way to look at how and why people choose to use or not use a technology is social information processing. This theory begins by critiquing rational choice models, which presume that people make adoption decisions and other evaluations of technologies based upon objective characteristics of the technology. In order to understand social information processing, you first have to look at a few rational choice models.

One model, social presence theory, categorizes communication media based on a continuum of how the medium “facilitates awareness of the other person and interpersonal relationships during the interaction” (Fulk, et al., 1990, p. 118).

Communication is most efficient when the social presence level of the medium best matches the interpersonal relationship required for the task at hand. For example, most people would propose marriage face-to-face instead of using a text message.

Another rational choice model is information richness theory. In this theory, media are also arranged on a continuum of richness in four areas: speed of feedback, types of channels employed, personalness of source, and richness of language carried (Fulk, et al., 1990). Face-to-face communications is the highest in social presence and information richness.

In information richness theory, the communication medium chosen is related to message ambiguity. If the message is ambiguous, then a richer medium is chosen. In this case, teaching someone how to dance would be better with an online video that illustrates the steps rather than just an audio podcast that describes the steps.

Social information processing theory goes beyond the rational choice models because it states that perceptions of media are “in part, subjective and socially constructed.” Although people may use objective standards in choosing communication media, use is also determined by subjective factors such as the attitudes of coworkers about the media and vicarious learning, or watching others’ experiences

Social influence is strongest in ambiguous situations. For example, the less people know about a medium, then the more likely they are to rely on social information in deciding to use it (Fulk, et al., 1987).

Think about whether you prefer a Macintosh or a Windows-based computer. Although you can probably list objective differences between the two, many of the important factors in your choice are based upon subjective factors such as which one is owned by friends and coworkers, the perceived usefulness of the computer, and advice you receive from people who can help you set up and maintain your computer. Think of the iPhone vs Android debate or Instagram vs Snapchat.

In the end, these social factors probably play a much more important role in your decision than “objective” factors such as processor speed, memory capacity and other technical specifications.

Uses and Gratifications

Uses and gratifications research is a descriptive approach that gives insight into what people do with technology. This approach sees the users as actively seeking to use different media to fulfill different needs (Rubin, 2002). The perspective focuses on “(1) the social and psychological origins of (2) needs, which generate (3) expectations of (4) the mass media or other sources, which lead to (5) differential patterns of media exposure (or engagement in other activities), resulting in (6) needs gratifications and (7) other consequences, perhaps mostly unintended ones” (Katz, et al., 1974, p. 20).

Uses and gratifications research surveys audiences about why they choose to use different types of media. For example, uses and gratifications of television studies have found that people watch television for information, relaxation, to pass time, by habit, excitement, and for social utility (Rubin, 2002).

This approach is also useful for comparing the uses and gratifications between media, as illustrated by studies of the World Wide Web (www and television gratifications that found that, although there are some similarities such as entertainment and to pass time, they are also very different on other variables such as companionship, where the Web was much lower than for television (Ferguson & Perse, 2000). Uses and gratifications studies have examined a multitude of communication technologies including mobile phones (Wei, 2006), radio (Towers, 1987), satellite television (Etefa, 2005), and social media (Raacke & Bonds-Raacke, 2008; Phua, Jin & Kim, 2017).

Media System Dependency Theory

Often confused with uses and gratifications, media system dependency theory is “an ecological theory that attempts to explore and explain the role of media in society by examining dependency relations within and across levels of analysis” (Grant, et al., 1991, p. 774). The key to this theory is the focus it provides on the dependency relationships that result from the interplay between resources and goals.

The theory suggests that, in order to understand the role of a medium, you have to look at relationships at multiple levels of analysis, including the individual level—the audience, the organizational level, the media system level, and society in general.

These dependency relationships can be symmetrical or asymmetrical. For example, the dependency relationship between audiences and network television is asymmetrical because an individual audience member may depend more on network television to reach his or her goal than the television networks depend on that one audience member to reach their goals.

A typology of individual media dependency relations was developed by Ball-Rokeach & DeFleur (1976) to help understand the range of goals that individuals have when they use the media. There are six dimensions: social understanding, self-understanding, action orientation, interaction orientation, solitary play, and social play.

Social understanding is learning about the world around you, while self-understanding is learning about yourself. Action orientation is learning about specific behaviors, while interaction orientation is about learning about specific behaviors involving other people. Solitary play is entertaining yourself alone, while social play is using media as a focus for social interaction.

Research on individual media system dependency relationships has demonstrated that people have different dependency relationships with different media. For example, Meadows (1997) found that women had stronger social understanding dependencies for television than magazines, but stronger self-understanding dependencies for magazines than television.

In the early days of television shopping (when it was considered “new technology”), Grant, et al. (1991) applied media system dependency theory to the phenomenon. Their analysis explored two dimensions: how TV shopping changed organizational dependency relations within the television industry and how and why individual users watched television shopping programs.

By applying a theory that addressed multiple levels of analysis, a greater understanding of the new technology was obtained than if a theory that focused on only one level had been applied.

Social Learning Theory/Social Cognitive Theory

Social learning theory focuses on how people learn by modeling others (Bandura, 2001). This observational learning occurs when watching another person model the behavior. It also happens with symbolic modeling, modeling that happens by watching the behavior modeled on a television or computer screen. So, a person can learn how to fry an egg by watching another person fry an egg in person or on a video.

Learning happens within a social context. People learn by watching others, but they may or may not perform the behavior. Learning happens, though, whether the behavior is imitated or not.

Reinforcement and punishment play a role in whether or not the modeled behavior is performed. If the behavior is reinforced, then the learner is more likely to perform the behavior. For example, if a student is successful using online resources for a presentation, other students watching the presentation will be more likely to use online resources.

On the other hand, if the action is punished, then the modeling is less likely to result in the behavior. To illustrate, if a character drives drunk and gets arrested on a television program, then that modeled behavior is less likely to be performed by viewers of that program.

Reinforcement and punishment is not that simple though. This is where cognition comes in—learners think about the consequences of performing that behavior. This is why a person may play Grand Theft Auto and steal cars in the videogame, but will not then go out and steal a car in real life. Self-regulation is an important factor. Self-efficacy is another important dimension: learners must believe that they can perform the behavior.

Social learning/cognitive theory, then, is a useful framework for examining not only the effects of communication media, but also the adoption of communication technologies (Bandura, 2001).

The content that is consumed through communication technologies contains symbolic models of behavior that are both functional and dysfunctional. If viewers model the behavior in the content, then some form of observational learning is occurring.

A lot of advertising works this way. A celebrity uses a new shampoo and then is admired by others. This message models a positive reinforcement of using the shampoo. Cognitively, the viewer then thinks about the consequences of using the shampoo.

Modeling can happen with live models and symbolic models. For example, a person can watch another playing Just Dance, 2016, a videogame where the player has to mimic the dance moves of an avatar in the game. The other player considers the consequences of this modeling.

In addition, if the other person had not played with this gaming system, watching the other person play with the system and enjoy the experience will make it more likely that he or she will adopt the system. Therefore, social learning/cognitive theory can be used to facilitate the adoption of new technologies and to understand why some technologies are adopted and why some are adopted faster than others (Bandura, 2001).

The Theory of the Long Tail

Former Wired Magazine editor Chris Anderson developed the theory of the long tail. While some claim this is not a “theory,” it is nonetheless a useful framework for understanding new media markets.

This theory begins with the realization that there are not any huge hit movies, television shows, and albums like there used to be. What counts as a hit TV show today, for example, would be a failed show just 15 years ago.

One of the reasons for this is choice: 40 years ago, viewers had a choice of only a few television channels. Today, you can have hundreds of channels of video programming on cable or satellite and limitless amounts of video programming on the Internet.

New communication technologies are giving users access to niche content. There is more music, video, video games, news, etc. than ever before because the distribution is no longer limited to the traditional mass media of over-the-air broadcasting, newspapers, etc. or the shelf space at a local retailer.

The theory states that, “our culture and economy are increasingly shifting away from a focus on a relatively small number of ‘hits’ at the headend of the demand curve and toward a huge number of niches in the tail” (Anderson, n.d.).

Figure 3.3 shows a traditional demand curve; most of the hits are at the head of the curve, but there is still demand as you go into the tail. There is a demand for niche content and there are opportunities for businesses that deliver content in the long tail.

Source: Anderson (n.d.)

Both physical media and traditional retail have limitations. For example, there is only so much shelf space in the store. Therefore, the store, in order to maximize profit, is only going to stock the products most likely to sell. Digital content and distribution changes this.

For example, Amazon and Netflix can have huge inventories of hard-to-find titles, as opposed to a Red Box kiosk, which has to have duplicate inventories at each location. All digital services, such as the iTunes store, completely eliminate physical media. You purchase and download the content digitally, and there is no need for a warehouse to store DVDs and CDs.

Because of these efficiencies, these businesses can better serve niche markets. Taken one at a time, these niche markets may not generate significant revenue but when they are aggregated, these markets are significant.

Anderson (2006) suggests rules for long tail businesses. Make everything available, lower the price, and help people find it. Traditional media are responding to these services. For example, Nintendo is making classic games available for download. Network television is putting up entire series of television programming on the Internet.

The audience is changing, and expectations for content selection and availability are changing. The audience today, Anderson argues, wants what they want, when they want it, and how they want it.

The Principle of Relative Constancy

So now that people have all of this choice of content, delivery mode, etc., what happens to older media? Do people just keep adding new entertainment media, or do they adjust by dropping one form in favor of another?

This question is at the core of the principle of relative constancy, which says that people spend a constant fraction of their disposable income on mass media over time.

People do, however, alter their spending on mass media categories when new services/products are introduced (McCombs & Nolan, 1992). What this means is that, if a new media technology is introduced, in order for adoption to happen, the new technology has to be compelling enough for the adopter to give up something else.

For example, a person who signs up for Netflix may spend less money on movie tickets. A Spotify user will spend less money purchasing music downloads or CDs. So, when considering a new media technology, the relative advantage it has over existing service must be considered, along with other characteristics of the technology discussed earlier in this chapter. Remember, the money users spend on any new technology has to come from somewhere.