Diffusion  of  Innovation  Theory

The Diffusion of Innovation Theory has been studied to a great extent in the literature. The model by Rogers, as first introduced in 1962, has been very popular for analysing technological innovation adoption (Cheng et al., 2004). The innovation-decision process that leads to adoption is a sequence of steps from initially gaining knowledge of an innovation, to forming an attitude toward it, to make a decision to adopt or reject it, to use it, and at last reinforcing this decision (Rogers, 2003).

Diffusion is defined as “the process in which an innovation is communicated through certain channels over time among the members of a social system” (Rogers, 2003, p.5). Four main elements that affect adoption of an innovation is the innovation itself, communication channels, time and the social system. An innovation can be in the format of an idea, practice or object, and the degree of newness of an idea as perceived by an individual determines his or her reaction to it. If it is perceived as new, it is an innovation (Rogers, 1995). Even though the behaviour of visiting a consumer fair does not fit the definition of innovation perfectly, it can still be assumed to be an innovation since there does not exist consumer fairs for jewellery in Norway today.

Communication is the process of sharing and creating information in order for both parties to develop a mutual understanding of what is being communicated. This particular communication represents a type of communication where the messages are related to a new idea. It is the newness of the idea that makes diffusion special (Rogers, 2003).

Diffusion is a type of social change, which can be defined as “the process by which alteration occurs in the structure and function of a social system” (Rogers, 2003, p.6). A social system is a group of units, all related to each other, which are engaged to solve a problem together and to reach a common goal. The members of the social system can be individuals, informal groups, organizations, and subsystems (Rogers, 1995).

The rate of adoption refers to the rapidity of adopting an innovation in a social system.

Innovations adopted by an individual are often adopted more rapidly than those adopted by organizations. Thus, the rate of adoption decreases, as more people are involved in an innovation-decision. Rogers (2003) distinguish between five variables that all affect the rate of adoption of an innovation. These are; perceived attributes of an innovation, the type of innovation decision, the nature of communication channels diffusing the innovation, and the extent of promotional efforts by change agents. The variable “attributes of an innovations”

has been investigated to a larger extent than the other variables, and it explains about half of the variance in the rate of adoption of an innovation (Rogers, 2003). In this thesis I will focus on perceived attributes of an innovation from the consumer perspective to identify potential drives and motivators to adopt an innovation, such as visiting a consumer fair for jewellery.

3.3.4.1 Perceived attributes of an innovation

The characteristics of the variable “perceived attributes of an innovation” are based on past writing and research by Rogers (2003). They are all to a certain degree empirically interrelated to the others, however at the same time conceptually different. The five characteristics are; relative advantage, compatibility, complexity, trialability, and observability (Rogers, 1995). According to Rogers (2003) they can explain from 49 to 87 percent of the variance in the rate of adoption, which makes perceived attributes of an innovation an important variable when explaining the intention of a consumer to adopt an innovation. On the contrary, as I will discuss later, not all studies have found explained variance to be above 49 percent.

Relative advantage refers to whether or not an innovation is perceived as better than the idea it is based on. Relative advantage can be considered from an economic perspective or in terms of social prestige, convenience and satisfaction. How advantageous an individual perceives an innovation is of great importance, and influences the rate of adoption (Rogers, 1995). It is in the interest of consumers to gather information about an innovation to reduce uncertainty, and to acquire knowledge about how much better an innovation is than an existing one. The likelihood of adoption is greater if an innovation is perceived as superior to competitors. When communicating an innovation, the relative advantage is often an important part of the message (Rogers, 1995).

According to Rogers (1995) researchers have found the relative advantage characteristic to be one of the top predictors of the rate of adoption of an innovation, with a positive relationship between them. Economic factors may influence rate of adoption by decreasing the price of a product over time. At the introduction of an innovation the price will be many times higher than say five years into the future. While the price declines the adoption rate increases. Tarde (1903, as cited in Rogers, 2003) made an observation about the search of higher status as a reason to copy the behaviour of others. For some products, such as for instance fashion apparel, the social and status aspect is the main advantage.

The relative advantage of an innovation may lead to overadoption. Overadoption occurs when an individual adopts an innovation even though experts feel that the innovation should not be adopted (Rogers, 2003). This situation may occur as a result of insufficient knowledge about the innovation, inability to predict consequences from adopting it, or as a result of the status-conferring aspect. Some consumers will adopt innovations when they should reject them simply because they thrive on change and new ideas, or because one attribute is so attractive that it outshines other assessments (Rogers, 2003).

Compatibility reflects how the individual perceive the innovation to be consistent with existing values, past experiences and needs. The more compatible an innovation is the more rapidly will it be adopted. Compatibility means less uncertainty for the adopter; it fits better with the life situation of the consumer and appears more familiar. This will increase the rate of adoption. Research have shown the compatibility component to be less important than relative advantage when predicting the rate of adoption, however it is central as new ideas often are compared to what already exists (Rogers, 2003).

In order for individuals to adopt an incompatible innovation they will have to change their value system, which is a time-consuming process (Rogers, 1995). Change and adoption can be very difficult when introducing an innovation to people with strongly held values.

Cultural incompatibility refers to a situation where an innovation meant for one culture spreads to another with different values.

The ideas that a consumer holds in memory can affect how he or she adopts a new idea. The past experiences impacts how one judges and interprets a new idea. For an idea to be an innovation there should be an incompatibility. If an idea is highly compatible it does not represent a large amount of change (Rogers, 2003).

Complexity refers to the degree of difficulty in understanding and using an innovation.

Some ideas will require the individual to develop new skills and understanding resulting in a slower adoption process. New ideas that are easy to comprehend will be adopted more quickly (Rogers, 1995).

In many innovations complexity is of less importance than relative advantage and compatibility, however it can be a constraint for other types of innovations. An example of such an innovation was the introduction of the home computer in consumer homes. They did not have the technical expertise to understand how to operate the innovation, which led to a slower rate of adoption (Rogers, 2003).

Trialability refers to the ability of trying and experiencing an innovation before adopting it.

If a consumer can try an innovation on the instalment plan it will contribute to a quicker rate of adoption compared to innovations that are not available for trial (Rogers, 1995). Rogers (1995) claim that if an innovation is trialable, it reduces the amount of uncertainty for the adopter as it can learn by doing. Thus, it becomes easier for the consumer to use the innovation, and learn how to use it in a consumer setting. This is not always possible as certain innovations are difficult to try in advance (Rogers, 2003), such as visiting a consumer fair. Some products on the other hand can be designed so that they enable trial and thus increase the adoption-rate.

Trialability is not equally important for all consumers. Consumers that are early adopters want trialability because they do not have a large degree of prior knowledge, experience, and information, as a foundation for adoption. Consumers that adopt at a later point in time can

gain information from the consumers that already adopted the innovation, and therefore their own personal trial is of less importance (Rogers, 2003).

Observability reflects how visible the results of an innovation are to others. If it is easy for consumers to see the results of an innovation it will increase the likelihood of them adopting it (Rogers, 2003). If one adopter communicates to others about an innovation, it can lead to others adopting it too. Visibility enables other consumers to attain information from friends, family and acquaintances as it stimulates discussion and interest. If an innovation is difficult to observe, it will decrease the rate of adoption. Rogers (2003) points out the example of

“safer sex” to avoid contracting HIV and AIDS. As safe sex is fairly ambiguous in its meaning it led to this preventive innovation spreading slowly and reaching a small number of those who are at risk of contracting these diseases (Singhal and Rogers, 2002; Rogers, 2003).

3.3.4.2 Strengths and limitations

Diffusion theory has mainly been used to explain technological innovations, however a study by Chatman (1986) on the diffusion of job information imply that the theory can be used in other behaviour studies by applying modifications to the theory.

As a result of many studies on diffusion, all elements of the diffusion process have been addressed. Criticism has been raised due to similar methods used in the analyses (Bell, 2006). A problem that can occur is the pro-innovation bias referring to the assumption that the relative advantage is always positive. Another problem is related to recall and that consumers may not be able to recall exactly when they adopted the innovation. Furthermore, the theory is criticised for mainly investigating durable goods, however proving to be successful. For goods that are not durable goods, modifications of the theory might be required (Bell, 2006). Rogers (2003) highlights other areas where the theory has been criticized. A bias that can occur is the individual-blame bias, which is “the tendency to hold an individual responsible for his or her problems, rather than the system of which the individual is a part (Rogers, 2003, p. 119). Thus, the individual is blamed rather than for instance the producer of a good, in cases where a problem is caused by a larger context than that of the individual (Rogers, 2003). Another problem that may occur in diffusion research is related to recall.

Rogers (2003) claims that the perceived attributes of an innovation explains between 49 and 87 percent of the variance in the rate of adoption. On the contrary, some studies show results conflicting with this claim. A study by Karahanna et al. (1999) find perceived attributes (influencing attitude) together with subjective norm and perceived voluntariness to explain only 38,4 percent of variance in behavioural intention to adopt an information technology.

Schneider (2007) investigates four innovation attributes within administrative practises and finds them to explain 31 percent of variance in adoption. Another study by Pechtl (2003) on adoption of online shopping behaviour found perceived innovation attributes to explain 39 percent of variation in adoption. These results show that the variance explained by the perceived attributes in fact prove to be lower in some studies than what Rogers (2003) claim.

Most of the research on diffusion studies individuals and their perception of an innovation with environmental factors influencing the adoption process. Few researchers have investigated the characteristics of an innovation as the determinant of adoption (Wejnert, 2002). An analysis conducted by Wejnert (2002) finds that to better understand the adoption process, the diffusion theory must be extended to include the interactive impact of variables, the influence of one variable on other variables, and the threshold of an individual of adopting in relation to individual characteristics. The individual as modulator of adoption has received little attention in diffusion research, as most research focus on information about an innovation available to the individual (Wejnert, 2002).