Las movilizaciones, los actos y las “tomas”

Following the development of a model, Rogers developed an innovation diffusion theory which posits that for any idea, practice, or object that is perceived as new by an individual or other unit of adoption to be communicated through certain channels over time among the members of a social system, there must be four elements of the innovation itself: communication channels for that innovation, time, and the social system (context) which all

Figure 3.1: Rogers Adoption and Innovation Curve

Source: www.valuebasedmanagement.net/methods rogers innovation adoption curve.h

determine its rate of adoption (Rogers 2003: 436/1995: 1-2). The theory adds that an innovation’s adoption rate is affected by relative advantage, compatibility, complexity, trialability and observability to those individuals within the social system. The more the participants of such an innovation create and share information with one another in order to reach a mutual understanding, the faster the adoption rate of the new innovation.

Rogers (1995: 1-2) and Hernandez, Jimenez and Martin (2010) add that because a communication channel is the means by which messages get from one individual to another, mass media channels are more effective in creating knowledge of innovations, whereas interpersonal channels are more effective in forming and changing attitudes toward a new idea, and thus in influencing the decision to adopt or reject a new idea. Most individuals evaluate an innovation, not on the basis of scientific research by experts, but through the subjective evaluations of near-peers who have adopted the innovation. Rogers (2003) argues that for a new innovation to be adopted, time has to elapse for the process ofinnovation- decision-makingwhich is the mental process through which individuals or institutions move from their initial knowledge about the innovation to forming an attitude towards the innovation, decision to adopt or reject, implementation of the innovation, and finally confirming the benefits of such innovation.

The Innovation Diffusion Theory (IDT) is concerned with the manner in which a new technological idea, artefact or techniques or a new use of an old technique, migrates from creation to use (Rogers 1995; 2003). In this theory technological innovation is communicated through particular channels, over time, among the members of a social system (Clerk 1999). The main goal of IDT is to understand the adoption of innovation in terms of four elements of diffusion including innovation, time, communication channels, and social systems (Clerk 1999). According to this theory, an individual’s behaviour in relation to adoption of technology is determined by his or her perceptions regarding the relative advantage, compatibility, complexity, trial ability, and observation ability of the innovation, as well as social norms (Rogers 2003: 436).

A number of studies have used the IDT as their theoretical framework such as Youngseek Kim and Kevin Crowston(2012) who, in their study on Technology adoption and use of theory review for studying scientists' continued use of cyber-infrastructure, identified factors that might increase the likelihood of adoption. Another study by Surry, D.W. & Farquhar, J.D. (May 1997) in their study of Diffusion theory and instructional technology discusses how theories of innovation diffusion have been incorporated into instructional technology.

Information Systems scholars mentioned that in the context of end-user computing many of the classical diffusion assertions were valid (Ritu, Agarwal & Prasad 1997; Brancheau & Wetherbe 1990). The five main constructs of IDT were employed and found to have significant relationships with other factors in ICT adoption. Relative advantage was found to have a positive relationship with attitude (Agarwal & Prasad 2000), and relative usage intention (Lin, Chan & Wei 2006). Compatibility was found to influence Perceived Usefulness (A Bhattacherjee & Hikmet 2007), PEOU (Hernandez, Jimenez & Martin 2010), attitude (Ritu Agarwal & Prasad, 2000; Lee, Kozar & Larsen 2003) and intention (Saeed & Muthitacharoen 2008; Wu & Wang 2005). Complexity was found to have a negative relationship with the technology adoption intention (Beatty, Shim & Jones 2001; Son & Benbasat 2007).

Moreover, innovation has been described as an idea, a product, a technology, or a program that is new to the adopting unit. The diffusion of innovation theory suggests that perceptions of technology characteristics, such as its relative advantage, compatibility, complexity, trialability, and observability impact the adoption of any new product. A number of researchers have applied Rogers’ theory in their studies, for instance Raisinghani and Schkade (1998) to explain the adoption of Internet, intranet, extranet technologies for electronic commerce applications, and Tan and Teo (2000) to describe factors influencing the adoption of internet banking in Singapore.

The Innovation Diffusion Theory (IDT) developed by Rogers (2003) has also been employed by students studying individuals’ technology adoption. While this theory can be seen to be bringing in the dimension of the reasons as to why individuals adopt technology, Scholars such as Damanpour (1996), Plsek and Greenhalgh (2001), Downs and Mohr (1976), and Lyytinen and Damsgaard (1998) argue that technologies are discrete packages developed by independent and neutral innovators, and technologies diffuse in a homogenous fixed social ether called a diffusion arena which is separate from the innovations locale. The diffusion rate is a function of push and pull; it is difficult to quantify diffusion because humans and human networks are complex, Damanpour (1996), Plsek and Greenhalgh (2001), Downs and Mohr (1976), and Lyytinen and Damsgaard (1998). Measuring what exactly causes adoption of an innovation is extremely difficult, if not impossible. The same scholars also assert that diffusion theories can never account for all variables, and therefore might miss critical predictors of adoption and thevariety of variables which has led to inconsistent results in

research and has consequently reduced its heuristic value. Green (2004) critiqued IDT by arguing that the diffusion of a practise depends on the decisive justifications used to rationalise.Compagni ,Mela and Ravasi (2000) stated that early experience with the implementation of an innovation influences later adoptions. These practices eventually trigger and support the isomorphic diffusion of the innovations even in the presence of persistent and certainty about its technical or economic benefits (Compagni ,Mela and Ravasi 2000).