Innovation Diffusion

The most widely cited framework for examining this topic can be found in verett Rogers’ innovation diffusion theory (Rogers, 1962). Rogers’ (1962) original study of innovation in agriculture has formed the basis of most studies related to adoption and diffusion and four of his main ideas concern the diffusion process itself, the categories of people adopting an innovation, the attributes of the innovation and the rate at which an innovation is adopted. The diffusion process outlined by Rogers has five stages – knowledge, persuasion, decision, implementation and confirmation. Potential adopters have to be persuaded to utilise an innovation and following some trial period, adopters need to decide whether they continue to use the innovation or stop using it altogether. Diffusion is thus not a momentary act but an on-going process that can be studied,

to adopt a particular innovation from the “innovators” who take the lead to the “laggards” who resist adopting the innovation for as long as possible. Attributes of an innovation are used to describe the suitability of an innovation for

adoption. An innovation is more likely to be adopted if the potential adopters perceive it is easy to try out, is compatible with their personal and professional goals, is simple to use, is better than the status quo and has demonstrable benefits (Rogers, 2003).

Rogers’ work appears to be a good basis for investigating a new innovation such as m-learning but in a comprehensive review of diffusion research, he points to a challenge in investigating new technology innovation. That is the pro-change bias of the researcher which tends to ‘assume the innovations studied are “good” and should be adopted by everyone’ Rogers 19 6 p.295). This is perhaps a serendipitous warning for this research as m-learning projects are by their pilot and research nature ‘early adopters’ and likely to have champions who believe the technology is certain to both succeed and embed at unrealistic rates. Rogers also points to the problems of ‘one-shot surveys’ in drawing conclusions;

diffusion is a time based process and this implies research needs to look at adoption from initial use through to widespread usage in an organization. This is another challenge for this research, in that it was never likely that it could

observe m-learning projects from early adoption through to any form of majority usage within an institution; effective embedding strategies would have to be judged as those that are most likely to succeed in the longer term. Looking for evidence that m-learning projects are feeding into institutional ICT strategy is a more appropriate area to examine.

In trying to use innovation diffusion theory as a framework for studying m- learning adoption it is appropriate to look at how Rogers’ work has been used in studies of adoption of information systems and especially information systems to support student learning. Building on innovation diffusion, there are numerous studies covering the adoption of information technology, telecommunications and even wireless internet itself (e.g. Gurbuxani, 1990, Grover and Goslar, 1993, Malhotra and Segars, 2005). In a quantitative study of adoption of computerised manufacturing inventory control systems, factors which affected the adoption process were: the user community, characteristics of the organization,

complexity of the technology, the task to which the technology is being applied and the organization environment (Cooper and Zmud, 1993). Therefore, in investigating university use of m-learning, it will be important to identify the benefits of the technology and how they fit into the institution’s strategy. Similarly, a study of mobile internet usage in the USA utilised Rogers’s innovation diffusion work to place users into five categories: innovators, early adopters, early majority, late adopters and laggards (Malhotra and Segars, 2005). Using a quantitative approach they were able to group the users into Rogers’ categories and then evaluate each group’s mobile internet usage. They concluded that an evolutionary approach is required to introduce new services and that organisations need to carefully segment their service offerings in order to capture a wide user base. Since the definition of m-learning adopted for this study is any student use in support of their studies, it could be argued that segmenting and differentiating different users needs is not especially relevant. However there may be some significance that universities who offer more services to their users

(students) be they learning or administration features, may be making more progress towards embedding m-learning within their ICT strategy.

The innovation decision process may be seen as a temporal sequence of steps through which an individual passes from initial knowledge of that innovation to forming a positive or negative attitude towards it, to a decision to adopt or reject and finally through the adoption trajectory to embedded use (Rogers, 2003). The concept of adoption or rejection is perhaps too absolute in the case of

Information Systems as in practice users may adapt an innovation to their own needs rather than accept or reject what is on offer. In research that combined innovation diffusion and attitude theories in an IT context, the beliefs and attitudes of users in pre-adoption and post-adoption (continued use) situation were examined (Karahanna et al., 1999). Pre-adoption attitudes are based on Rogers’ set of innovation characteristics which affect the perception of the innovation prior to adoption and may affect the rate at which the innovation is adopted. The result of their study shows that post-adoption attitudes are based on social beliefs of how useful the innovation is and how using it will enhance the image of the user. This may be too simplistic a statement when it comes to a university. It will be important to identify that there are categories of user in a university with different attitudes and values including academic staff, students, administrators, IT service providers and information providers, such as

librarians. Staff may have long-term interests in using an innovation, such as career enhancement, whereas students may simply have very short-term goals such as using the technology to complete a course module successfully.

how the technology enhances the social standing and image of the student that can use the technology as per Karahanna et als’ study but more by the social and career status of the academic staff. So a possible interesting research question and area that could expand current thinking on IS innovation in HE is the question whether staff ‘steer’ student adoption or could the inverse be true students may ‘pull’ staff adoption in response to student needs? And what level of staff might be involved in this process; academic tutors? Or might it grab the attention of senior management and executive members (see section 3.4 for discussion of research questions)?

From a different standpoint Malhotra and Segars talk about the ‘Behavioural Compatibility’ where the innovation needs to be consistent with adopters’ existing values and past experiences. With a more radical change in the way of working that mobile technologies can bring, other than the innovators of m-

learning themselves, the early adopters may perceive a high level of behavioural

change is required to use the new technology. Those proposing new wireless based services must convince the early adopters that the behavioural change is not as extensive as they perceive (Malhotra and Segars, 2005, p. 108). The initial field study (see Chapter 4) indicated that the predominant end-user, students, could be willing adopters but that there might be staff resistance for both cultural and lack of IT skills reasons. There is also the issue of the compatibility with the organization; m-learning challenging the working practices of areas such as procurement and IT. In early field research, IT

seen as a core part of IS strategy and therefore not worthy of investing significant effort in.

Innovators by their nature are more venturesome and have a ‘desire for the rash the daring and the risky’ (Rogers, 2003, p. 283). They have a ‘more favourable attitude towards change than do later adopters’ (Rogers, 2003, p. 290) so may not expect the resistance they encounter. Rogers highlights the importance of early adopters as ‘having the highest degree of opinion leadership in most systems’ (Rogers, 2003, p. 283). This is another important factor that this research will look at to see how these barriers are overcome in introducing m-learning into a university and what strategies succeed or fail. Both the initial field study and the literature demonstrate that many initial m-learning projects are funded through short-term research grants (JISC, 2005, Traxler, 2013). How will they be embedded and developed once the research funding ends and they require university investment to continue? How will the projects progress from experimental pilots to make that link with overall IS strategy to become core services?

Cooper and Zmud also highlight the impact of organizational politics on an innovation where ‘rational actions serve as facades to mask political motives and to legitimise self interest’ (Cooper and Zmud, 1993, p. 136). The negative impact of politics on the success of an innovation is also discussed in a paper looking at new product development (Jones and Stevens, 1999). These political interests may be significant in this research into university environments which are subject to competition between academics for both position and research

funding, invisible pecking orders and sometimes very public disagreements (Becher and Trowler, 2001). The initial field study suggested that political positioning may play a part in both individuals who promote the technology and those who resist its introduction. An individual’s response to an innovation in a free market (e.g. adopting a new product personally) may differ from their response when constrained by an organizational hierarchy (Rogers, 2003). The research needs to take care not to assume that all m- learning adoptions are the result of a rational choice of the individuals involved, nor will their behaviour be necessarily rational when asked to provide resources to the project and this also highlights the importance of gaining data from more than one institution to try to illuminate common barriers.

Rogers’ theory is very much centred on the innovation itself and therefore doesn’t focus strongly on political aspects associated with change, the drive towards diffusion are very much dependent on the characteristics of the

innovation itself. Rogers does look at the characteristics of both innovators and adopters (Rogers, 1995, p.267) but again this is with reference to the innovation itself. From the literature on m-learning and the researchers’ own initial field study, there appeared to be no shortage of staff within HE willing to investigate

m-learning and an apparent audience of tech-savvy students or ‘digital natives’

(Prensky, 2001) willing to give this a try. What seemed to be missing was an examination of the challenges of negotiating an m-learning innovation through the complex political agendas that exist within institutions and the somewhat distributed nature of the various institutional strategies such as IT and Teaching and Learning. What was needed was to look more at the people aspects of

dealing with m-learning and how the various organizational functions that constitute an institution, might cooperate to create successful embedding. Consideration of the researcher’s prior knowledge also indicated that a theory that would illuminate the different needs of stakeholders could offer a better approach.

There are theories and models that look more at the reactions of people to innovations such as Actor-Network Theory (Law and Hassard, 1999). Actor- Network Theory (ANT) has the concept of ‘agency’ (Latour, 2005) and states that agency resides both in people and objects such as innovations. It insists that all entities, both human and non-human, be subjected to the same process of social analysis (Law, 1994). ANT identifies the set of processes involved in projects of social ordering as networks and looks at the changes that take place in those networks through a project. ANT has the concept of translation where the people, objects and processes have specific needs which then get translated into more general and unified needs so that needs are all met by one solution. When a system is up and running it gets adopted by the users by translating it into their own context and reflecting their work tasks and situations (Latour, 2005). It also has the concept of irreversibility where a network is established and can resist competing translations and therefore the change becomes irreversible. Actor- Network Theory may provide a useful model for looking at m-learning in higher education as the various actors (the university, teachers, students, IT services, the innovation itself etc.) go through a process of translation in order to find a stable way of working together. Are there important differences that this will identify between universities that successfully embrace and implement m-learning and

those that are unable to ‘translate’ irreversibly? The possibility of viewing the local m-learning project and the university IT organization as networks that will need to intersect, fits well with the definition of embedding discussed earlier in this chapter. ANT appears to be a very promising lens for looking at the adoption of m-learning and a deeper examination of the ANT literature occurs later in this chapter (see section 3.3).

Aside from the actor-centred transformation view championed by ANT, there are many examples where information technology has been used to change the way that organizations work - the internet being an extreme example of radical changes to areas such as retail and travel (Hammer and Champy, 2001). Technology may be seen as an agent of institutional change and indeed m-

learning may ultimately lead to different ways of delivering courses and in turn

lead to a different structure and staffing needs. Innovation Diffusion theory is based on assuming that individuals make rational choices and weigh up the costs and benefits of an innovation in a systematic manner and from an individual standpoint (Redmond, 2003). The adaptive strategies of individuals will vary from those who like to take risks with new technologies to those who suspect that they may be an attempt to reduce costs and achieve a service with fewer resources notably staff, and in effect view the innovation as just a new labour control strategy (Braverman, 1974, Tinker, 2002). Students and staff will not simultaneously embrace change because individuals differ with respect to perceived risk/reward of adopting new technology (Redmond, 2003). Thus different universities may embrace m-learning at very different rates depending on their openness to new ways of working and the relative power of staff. This

resonates with an earlier discussion on the impact of politics on an innovation trajectory (Cooper and Zmud, 1993). There is also an echo here of Karahanna et.al.’s notion of social status and whether staff will ‘push’ the innovation on the students or whether the students will ‘pull’ the innovation into the university (Karahanna et al., 1999). In other words whose risk/reward needs will dominate, students or staff? The sort of thinking championed by Prensky’s model of digital immigrants and digital natives (Prensky, 2001) would suggest that staff will be slow to pick up these new technologies but that students will be a ready and willing tech savvy customer base, but will that reflect reality? Indeed the Prensky model has been critiqued as simplistic and other terms such as ‘Visitors’ and ‘Residents’ have been proposed using the metaphor of place removing the focus from the generational divide (White & Le Cornu, 2011). It also worth noting that training of students to use new learning technologies and training of staff to re-design pedagogy to utilise such technologies is a more significant issue than any perceived generational attitude (Beetham et. al, 2009). A final factor in the push/pull debate would be who owns the space in which learning can take place. The traditional university IT model is one of desktop PCs in drop-in centres where the university clearly owns and controls the IT environment. The mobile space is clearly going to be shared between the students with their personal range of mobile services and the university providing some of its own services. Will students welcome university applications on their own devices or will

universities try to implement a model where they provide advice and thus can exert control? The move towards personalised learning is already seen as a challenge to the traditional HE IT approach and it is widely predicted that a shift is in place in education where learners will use their own personalised devices as

opposed to institution provided equipment (Johnson and Adams, 2011, Nyqvist, 2012). The issue of student-owned versus university-provided device strategies is extensively covered in the Project MED case study (see Chapters 7 and 8) and summarised in Chapter 9 Section 9.1.

IT diffusion behaviour is also influenced by senior management support, the centralization or de-centralization of decision making, organization size and IT function size (Pervan et al., 2005). Organizations that are characterised by decentralized structures and less formalization are likely to be more innovative than highly centralized organizations which use formalized controls (Pervan et al., 2005). Similarly research into telecommunications technologies suggests that more of these tend to be evaluated and adopted in ‘decentralized cultures’

(Grover and Goslar, 1993, p. 154). In decentralised structures, knowledge and decision-making may be ‘located anywhere in the network’ (Burns and Stalker, 1961, p. 121). Conversely, you might expect centralization to favour efficient implementation and deployment. Grover and Goslar’s survey of U.S

organizations concluded that centralized decision-making, neither favoured innovation nor implementation when it came to telecommunications networks. Having dispersed groups of expertise across organizations tended to provide a natural coordination which actually assisted introducing new networking technologies (Grover and Goslar, 1993). Another survey of the diffusion of networking technology concluded that a key factor in fast diffusion was the ‘prior existence of a well defined community with shared interests’ (Gurbuxani, 1990, p. 74). In looking at m-learning in an HE context, there appear to be many different stakeholders involved, including students, teachers, researchers,

librarians, IT Services staff, finance and management. Adoption of m-learning is a potentially complex process which balances the requirements of this diversity of stakeholders. On the other hand, a lot of funding and the IT strategy in universities would appear to be centrally managed and controlled (Allen et al., 2002), so will it be a case of local decision making aiding the innovation or central decision making hindering the adoption? A new technology such as m-

learning may also prove to have a niche deployment rather than widespread

diffusion across all faculties and universities.

A possible model for looking at m-learning diffusion is found in work carried out by the Global Diffusion of the Internet Project (GDI). In a paper reviewing studies of internet diffusion in 25 countries, a model was developed with six dimensions which cover the sophistication of the users, the organizational infrastructure, the networking infrastructure, the geographic dispersion of the user base, the maximum potential user base and the adoption within a specific industry sector (Wolcott et al., 2001, p. 6). Some of these factors reinforce points already seen in the other literature; such as the way an organization is structured (Pervan et al., 2005, Rogers, 1962). However, the model adds new dimensions such as geographical dispersion which might provide an interesting research question in the university environment. Are universities with geographically dispersed campuses more likely to embrace the m-learning technology than those located on one site?

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When an organization purchases a new technology it doesn’t necessarily follow