Diffusion of Innovation Theory (DIT)
The diffusion of innovation theory (DIT) explores the mechanisms that shape the adoption of a new concept, product, practice or perspectives. It was popularized by Everett Rogers (Sahin, 2006). Rogers argued that only a few people are open to new ideas and are willing to adopt them for real life applications. As the early adopters influence more people to adopt the technology a critical mass of adopters develops, and the innovation gets diffused amongst the population until it reaches a saturation point (Sahin, 2006).
Rogers classified the adopters into five distinct categories. The innovators are technology enthusiasts and appreciate technology for its sake (Lyytinen & Damsgaard, 2001). The early adopters are visionaries who adopt the technology and act as opinion leaders. They are natural trendsetters and are attracted to high-risk high reward investments. They are not particularly cost sensitive (Lyytinen & Damsgaard, 2001). The early majority are the pragmatists who adopt innovations that make business sense and have proven reliability. They abhor complexity and only take advice from trusted friends. The late majority is very conservative but adopts technology due to peer pressure and economic necessity (Lyytinen & Damsgaard, 2001). They are very skeptical, cost-sensitive and only adopt innovations in order to keep up with the competition. The laggards are very skeptical of innovations and prefer the status quo and only invest in a technology if there are no viable alternatives (Lee, 2009). The theory asserts that the innovators make 2.5% of the users while early adopters make 13.5% of the users (Lyytinen & Damsgaard, 2001). Early and late majority users comprise 34% respectively while the laggards comprise 16 of the users. The theory aims to streamline innovations such that they meet the needs of all the five categories of users.
The theory considers peer networks to be an important component of the adoption of an application as it is through these networks that innovators and early adopters stimulate mass adoption of the technology. Rogers (cited in Lyytinen & Damsgaard, 2001) identified five predictors of the success of an innovation. The predictors are the relative advantage offered by a technology, observability, compatibility, complexity and trialability (Teo, 2011; Lyytinen & Damsgaard, 2001). Relative advantage is the potential of an innovation to be perceived as better than the technology it replaces. Compatibility is the degree to which an application is perceived to be compatible with the prevailing conditions, experience and the needs to potential users. Complexity relates to the degree to which an innovation is considered difficult to comprehend and use (Lyytinen & Damsgaard, 2001). Trialability is the degree to which an application can be tested and modified. Observability relates to quality of the innovation having high visibility such that it stimulates discussions among peers (Sanson-Fisher, 2004; Venkatesh et al. 2014). Rogers opined that the five factors account for 49-87% of the variations that exist among different innovations.