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have been overlooked in this study as the researcher searches for a model that has greater coverage and more explanatory power.
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experiences and values of adopters will aid its rapid adoption. It can, therefore, be concluded that both relative advantage and compatibility have a positive effect on adoption. Complexity, on the other hand, has a negative effect on adoption because the more complex the innovation is, the less likely it will be accepted (Grgurović, 2014). Complexity is defined as ‘the degree to which an innovation is perceived as relatively difficult to understand and use’ (Rogers, 2003, p. 257). The fourth attribute, trialability, is the degree to which a user can try out the innovation. This attribute too aids adoption in that users who are able to try out an innovation, possibly before adoption or purchase, would be able to know more about the innovation.
Most notably, the first two attributes of relative advantage and compatibility, that are known to assist in adoption, are explored hence the adopter would be able to make a more informed opinion about a particular innovation. Observability applies to the degree to which the innovation can be observed before adoption. This attribute is closely related to trialability.
Both attributes are similar in that the adopter has a close encounter with the innovation, something that may help the adopter in making the right decision regarding whether to adopt or reject an innovation.
2.3.2 Communication Channels
Communication channels are a crucial element in the adoption and diffusion of an innovation because they are a means through which potential adopters learn about an innovation, and more importantly, facilitate the innovation-decision process which essentially are the stages an individual goes through from the first contact with an innovation to its complete adoption (Grgurović, 2014). According to Barrette (2015), both the channel and source of communication affect diffusion. In terms of channel, adopters often receive initial information about the innovation through mass media channels, and subsequently through interpersonal interactions. Rogers (2003) argues that although mass media channels allow for more widespread dissemination of information, interpersonal channels are more influential on decision-making. Possibly the secret behind this, lies in the fact that the innovation-decision process includes some sort of persuasion which may only work effectively if the person passing on the information has direct contact with the one receiving the message. This is advantageous because the message may be modified based on need.
Grgurović (2014) indicates that communication is most likely to occur between homophilous individuals (people who share similar attributes, activities, beliefs, and so forth). However,
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the challenge for diffusion is that innovators of technology tend to be heterophilous (dissimilar) with the majority of potential adopters because they have more technological literacy than later adopters. It is, therefore, recommended that good candidates to communicate with potential adopters and facilitate adoption must be people who bridge the technological literacy gap yet are homophilous with potential adopters in other respects.
Ideally, the change agents should be community's opinion leaders who are known to have a big influence over their communities.
2.3.3 Time
Time is a very important element in the innovation-diffusion process as it spells out how the innovation is taken up by various groups within the social setting. Time is required for the diffusion process, particularly because it takes time for individuals to make decisions about whether or not to adopt an innovation (Winter, 2013). For instance, it may take a few months for one group to make a decision about adopting a particular innovation but for others this may take several years. Rogers (2003) describes five adopter categories based on the time when the individual adopts an innovation in relation to other individuals: innovators, early adopters, early majority, late majority, and laggards.
Innovators are the members of the system who are the first to introduce the new idea, and they require a shorter adoption period than any other category. They comprise 2.5% of the population (Cheng et al., 2004). Some other attributes which are associated with the innovators are:
(1) Venturesome, desire for the rash, the daring, and the risky
(2) Control of substantial financial resources to absorb possible loss from an unprofitable innovation.
(3) The ability to understand and apply complex technical knowledge, and (4) The ability to cope with a high degree of uncertainty about an innovation.
The second group of the early adopters comprise of 13.5% of the population (Cheng et al., 2004). They are the first to try the idea out, and serve as role models to other individuals (Rogers 2003). Rogers identified the Early Adopters as:
(1) Integrated part of the local social system,
(2) Having greatest degree of opinion leadership in most systems,
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(3) Serve as role models for other members or society, (4) Respected by peers, and
(5) Successful.
The early majority make up 34% of the population (Cheng et al., 2004). Rogers identified the following as characteristics of the early majority:
(1) Interact frequently with peers,
(2) Seldom hold positions of opinion leadership,
(3) One-third of the members of a system, making the early majority the largest category.
(4) Deliberate before adopting a new idea.
The late majority make up 34% of the population just like the early majority (Cheng et al., 2004). Characteristics of the Late Majority include:
(1) Make up one-third of the members of a system (2) Subjected to pressure from peers
(3) Look at economic necessity of an innovation (4) Are skeptical, and
(5) Cautious.
Laggards make up 16% of the population (Cheng et al., 2004). Rogers identified the following characteristics in the Laggards:
(1) Possess no opinion leadership, (2) Isolates themselves,
(3) Make reference to the past, (4) Are suspicious of innovations,
(5) Innovation-decision process is lengthy, and (6) Have limited resources.
2.3.4 The Social System/Context
The social context refers to the social network surrounding a potential adopter, opinion leaders within that network, and organizational characteristics (Greenhalgh et al., 2004;
Rogers, 2003). Barrette (2015) observes that different social structures may encourage or impede diffusion of particular innovations depending on the community’s social norms, individual’s innovativeness, their relative status, and their access to and effectiveness within
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the community’s networks. Both Greenhalgh et al. (2004) and Rogers (2003) postulate that it becomes increasingly likely that other members of a social network will adopt an innovation once it has been adopted by some individuals within the social network, especially if the early adopters hold positive opinions of the innovation. The role of opinion leaders is especially stated as being crucial in this regard. Opinion leaders, according to Rogers (2003), do not necessarily hold official leadership positions, but their influence often stems from informal leadership roles that are ascribed to them by peers. Opinion leaders, among others, have more exposure to people outside the immediate social network, greater accessibility to others, higher levels of
innovativeness, and somewhat higher socioeconomic statuses than do others in the social network. These are attributes that make them better able to influence people within their social setting.
User’s awareness of community social norms and the community’s support for innovation are another important driver of innovation (Barrette, 2015). Besides, certain organisational characteristics make it more or less likely that an innovation will be adopted.
Greenhalgh et al. (2004) wrote about a number of aspects of the organisational
climate that influence adoption rates. These include the size of the organisation, the availability of resources, the organisational hierarchy, the organisation’s capacity for new knowledge, and the general climate of openness to change.
2.3.5 Innovation Adoption Process
According to Hornor (1998), Rogers identifies five main stages in the innovation adoption process. These are: (1) awareness, (2) interest, (3) evaluation, (4) trial, and (5) adoption. In the awareness stage "the individual is exposed to the innovation but lacks complete information about it". At the interest or information stage "the individual becomes interested in the new idea and seeks additional information about it". At the evaluation stage the
"individual mentally applies the innovation to his present and anticipated future situation, and then decides whether or not to try it". During the trial stage "the individual makes full use of the innovation". At the adoption stage "the individual decides to continue the full use of the innovation".
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Various actors, particularly, opinion leaders play a critical role in the adoption process.
Rogers emphasises the importance of various communication channels at various stages of the diffusion process. Change agents are another factor in the diffusion process. Change agents success is seen to depend on effort, compatibility, empathy, ability to motivate adopters and successful exploitation of opinion leaders (Chaudhuri, 1994).
Characteristics of the innovation
● relative advantage
● compatibility
● complexity
● trialability
● observability
Communication channels Diffusion
● mass media
● interpersonal networks
Change agent effort
Nature of the social system
Figure 2. 2: Diffusion Elements in the Rogers Framework (Source: Chaudhuri, 1994)
2.3.6 Strengths of the DOI Theory
Diffusion of innovation theory holds that innovation diffusion is “a general process, not bound by the type of innovation studied, by who the adopters [are], or by place or culture” (Rogers, 2004, p. 16), such that the process through which an innovation becomes diffused has universal applications to all fields that develop innovations. This makes the model ideal or tied to the study of specific innovations.
Another notable aspect of the DOI theory is that it is well established and widely used in information technology (IT) diffusion-related research (Prescott & Conger, 1995). The popularity of the model is reflected in the fact that it has been used and revised several times
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(Cheng, 2004). Moreover, it is also the basis of most of the models that attempt to explain the factors affecting whether an innovation will be shared and adopted by other individuals and organisations (Aizstrauta et al., 2015).
Furthermore, DOI offers a broader and more comprehensive explanation of technology adoption. According to Barrette (2015), whilst TAM offers empirical evidence of the factors influencing a user’s intention to adopt a new technology, DOI considers how individuals reach the decision point, how they implement the technology after adopting it, and whether and how they decide to continue using it. Such an approach gives technology advocates a clearer understanding of the complexities and scope of the technology adoption process.
2.3.7 Weaknesses of the DOI Theory
DOI assumes that a new idea, product, or service is favourable and would be adopted at different times by the different categories of the adopters of the innovation (innovators, early adopters, early majority, late majority and laggards). Cua (2012) says that this is not always the case in real life arguing that although DOI postulates that 16% of the population has a favourable attitude toward innovation: the innovators (2.5%), and the early adopters or opinion leaders (13.5%); and the remaining 84% are negatively biased: 34% (misleadingly called the “early majorities”) can still be convinced to reduce their innovation resistance while the remaining 50% (so-called late majorities and laggards) remain non-adopters to the end. Rogers (2003) further notes that there is nothing “early” about the 34% majority, and the late majorities and laggards may actually never become adopters. This implies that DOI fails to fully explain the rate at which different categories of people adopt innovations as implied in the model.
Despite its popularity, researchers such as Fich-man and Kemerer (1997) and Newell et al.
(2001) have criticised the DOI for its bias towards the technological component of the adoption process. The two scholars have argued that other relevant contingent factors beyond the technical features of an innovation should be considered for deeper understanding to emerge. Bose and Luo (2011) have argued further that although DOI theory is a powerful descriptive tool, it is less strong in its explanatory power, and less useful still in predicting outcomes and providing guidance as to how to accelerate the rate of adoption. The DOI is not
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used to underpin this study because of these weaknesses notwithstanding its discussed strengths.