The review of diffusion theory helps identify factors to use in determining the enquiries which can be investigated to see how and why m-learning might be different than just another IS innovation. However, there is criticism of the use of diffusion in IS Research, notably that of Wastell and McMaster (McMaster and Wastell, 2005). The placing of an innovation at the centre of the theory tends to lead to research which assumes the innovation is in some privileged position and the theory may be prejudiced against those that may reject the innovation for reasons that are not necessarily objective or based on technology capability. Little attention might be given to more hidden motives – ‘the political agenda within the status quo remains neither problematized nor questioned’ (McMaster and Wastell, 2005, p. 396).
Actor-Network Theory has gained popularity as an IS research approach, particularly in looking at situations where technology is an agent of change. Studies include work by Walsham, McMaster and others (McMaster et al., 1999, Walsham and Sahay, 1999). There are also a number of articles that compare different theories used to investigate technical innovations, notably comparisons between ANT and Activity Theory (Miettinen, 1999) and ANT and structuration theory (Jones and Karsten, 2008). Activity Theory is extremely popular in the education world and has been widely applied to m-learning, notably in the work of Mike Sharples (Sharples et al., 2007). As a theory it is well positioned to look at learning solutions, breaking down learning tasks into a series of activities.
Supporters of Activity Theory who are critical of ANT point to problems of ‘generalized symmetry’ (Miettinen, 1999, p. 181). By symmetry they refer to the
importance placed in ANT on treating non-human actors as equal partners in the network. It is claimed that this can give innovations a dominant role in the analysis and perhaps marginalize the role of the human actors such as software engineers or end-users. In effect, one could view this problem in a similar way to the criticism of diffusion, placing too much emphasis on the power of the
innovation or the role of the innovator. However ANT makes no demand to place the technical artefact at the centre of the analysis but simply suggests that the researcher should ‘follow the actors’ (Latour, 2005, p. 227) to gain the necessary insights. Following a non-human actor or technical artefact is a process of looking into the interactions of the artefact with the human actors which would surely avoid placing too great a priority to the innovation. Spinuzzi’s (Spinuzzi, 2008) study of developing knowledge networks in US telecommunications organizations uses both ANT and Activity Theory to look at how a
telecommunication service provider works. He concludes that Activity Theory is better suited to looking at networks of learning and learning activities (a view clearly shared by many m-learning researchers such as Sharples (Sharples et. al, 2007) and Traxler (Traxler, 2007)) but that it had weaknesses in looking at links between networks ‘the boundary objects’ (Spinuzzi, 2008, p. 206). Spinuzzi felt that Activity Theory placed too much emphasis on development tasks, with not enough focus on the interactions between those tasks. As this research had a goal of looking at how m-learning projects became linked to overall university
strategy then links or boundary objects were a key focus and hence Activity Theory was not chosen.
What of structuration theory which has also been frequently used in analysing IS projects? Giddens’ structuration theory (Giddens, 1984) looks at the relationship between individuals and society rejecting the view that social phenomena are determined either by social structures or autonomous human action. Giddens proposes that social phenomena are the product of both social structure and human agency – people draw on social structure to determine their actions and in turn these actions produce and modify social structure. Structuration theory focuses on the agency of humans and does not include the concept of agency in objects, unlike ANT, although it does recognize the ability of technology to influence social structures. Structuration theory is seen as’ bypassing the structure/agency debate’ (Jones and Karsten, 2008, p. 146) as it represents a modernist view that ANT rejects (Latour, 1993b).
Structuration theory may be more helpful in examining technologies that are more established through repeated cycles of implementation and use, and where apparent order is made and re-made. The m-learning projects examined in this thesis were not at that stage. There seemed to be more potential to make a contribution using ANT’s concepts of translation and the existence of links between networks. The notion of boundary objects (Star and Griesmer, 1989) also seemed to represent an opportunity to look at how these early projects might build links into the common IS strategy for an institution- in effect a path to embedding.
The issues identified with m-learning can be best described by the diagram below. The diagram (Figure 3) shows that innovation can be initiated from a
number of sources – lecturers and students as individuals, funded projects or even as conscious investments in pilot services by the university itself. In order to thrive and embed, these smaller networks of actors experimenting with m-
learning need to engage with the institution on a wider level unless they are
completely self-sufficient. There exists a range of institutional actors with which an m-learning project must engage in order to embed and these could include policies, departments, committees, funders (both internal and external) or even powerful individuals within the senior management who may need to be in enrolled in some form of translation in order to implement m-learning that will embed. Tutors Local IT Department Managers Students IT Services IT Strategy Learning and Teaching Strategy Executive Ethics Comitttees M-Learning Technology
Project Actors Institutional Actors
Funders
If m-learning is considered a potentially disruptive innovation then, in order for it to succeed, the range of departments, staff and policies positioned in the diagram above will need to undergo change before it becomes an irreversible feature of the university environment. In Actor-Network Theory terms, these are all actors in a network and will have to undergo a set of translations. ‘Mediators and intermediaries’ must form relationships so that the processes and
departments above translate into a network (Latour, 2005, p. 40). If the technology is to become established then the actors must undergo irreversible change (Callon, 1991) and that degree of irreversibility will depend on whether it remains an isolated example or whether it embeds and starts ‘to shape and
determine subsequent translations’ (Callon, 1991, p. 159).
Whilst Actor-Network Theory and its notions of networks and translations would seem to lend itself well as a method of looking at how these barriers are
overcome, another part of ANT is even more promising. Looking at project failure in the aircraft manufacturing industry, Law and Callon proposed the concept of local and global networks and the boundaries between the two (Law and Callon, 1992). They identified three factors which influenced the success or failure of a project with the most significant being ‘the capacity of the project to build and maintain a global network which will for a time provide resources of various kinds in the expectation of an ultimate return’ (Law and Callon, 1992, p. 46). They also talk about points of passage between the two networks which again looks like a concept that would help bridge the dotted line in Figure 3 above. The effectiveness of points of passage could be a key issue in the
embedding of m-learning a concept that also appears as ‘boundary objects’ (Star and Griesmer, 1989, p. 388) in an earlier ANT-based study.
A simple instance of a ‘local network’ in m-learning could be for a student to innovate, a lecturer to support the innovation and their interaction to form a local network where the students and lecturers cooperate. A good example is using text messaging of questions in lectures, something which in isolation does not require other actors in the university to approve or participate in. But this process eventually interacts with the global network as the practice spreads to other lecturers/faculties and teaching and ethics committees and perhaps unions start to debate whether this is acceptable practice or whether there are student inclusion issues and the requirement to form an institution-wide policy emerges. Therefore significant factors will be the ability of the local network to build links with the global network and influence the global network to approve and support the innovation and develop institutional policies to support it. Actors, be they individuals or even artefacts, need to become points of passage between the two networks for that influence and support to be achieved. In addition, a further strength of this local/global network model is the temporal aspect in that it looks at project trajectories and our interest is the shifting focus, actors and fortunes of a project over time rather than the identification of a specific moment of
translation.
Having reviewed the ANT literature and identified the local/global model as a way forward, it is useful again to reflect on the meaning of the term embedding within the context of this research. The researcher’s tacit knowledge gives a
strong indication through prior experience that examining the strength of the links between a trial or pilot project and overall organization strategy would be a key area to explore in the field research. The Law/Callon model of the
local/global network appears to offer the opportunity to explore those links. In effect, embedding can be defined in the context of this research as evidence that findings from local projects are influencing global IT strategy. The existence and effectiveness of points of passage between the networks will be a focus for subsequent field research.