ANT’s methodological principle has no unique set of methods which it is associated with, but makes use of the same techniques as ethnography and case studies. So for this study, I used data collection instruments which are commonly used in ethnographic studies, namely;
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questionnaire comprising of closed and open ended questions, semi-structured interview schedule, observation schedules, and non-participant observation of online actives by the student designers. This mixture of data collection instruments was necessary because the task of following actors in a complex and dynamic network line Web 2.0-facilitated collaborative required me to be well equipped. I needed to trace the emergence of the web 2.0- facilitated collaborative design paying attention on the ever changing associations that kept or destroyed the emerging network. My focus was on how the design process was constituted through the associations formed among the actors.
After gaining access to the research site and the particular nodes, my next step was to map out how data were going to be gathered. My expectation was that the relevant research data would emerge from the associations that were formed among the actors in the collaborative design process. This is consistent with ANT which call for researchers to study networks from the actors’ point of view, that is, by simply following the actors (Latour, 2005). As such the underlying principle of my data collection was to “follow the actors” (Latour 2005). This involved tracing the network and its shifting ties which occur at the three nodes with respect to both the human (students) and non-human (the technologies used) actors without privileging one over the other. Data collection was a continuous process as the actors proceeded with their project.
The ANT dictum to “follow the actors” opens up the research field to an endlessness list of actors which could be followed. However, since my aim was to explore how student designers used Web 2.0 technology during the collaborative design process, I employed a maximum variation sampling strategy. This sampling strategy of purposeful sampling can help the researcher to illuminate significant common patterns across the variation (Patton, 1990).
Firstly, I created a list of potential human and nonhuman actors in the process. I followed the actors as they traversed the three nodes of the Web 2.0-faciltated collaborative design collecting traces of how they linked among themselves.
The notion of collaborative design being interactive and discursive (Schön, 1983) suggests that listening to actors’ voices is essential to this research. To map associations and assemblages that students formed during the collaborative design process, I followed actors by episodic interviews like an ethnographer (Van-Maanen, 2001). Episodic interviews are “based on the theoretical assumption that narratives are constituted experience rather experiences per-se”
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(Tabak, 2015, p. 62). Episodic interviews are relevant to ANT studies because they seek for knowledge which is linked to action and situational context such as time, space, person, event and situations. Flick (1997), highlights three important things that make episodic interviews to succeed:
1. The interviewer should combine questions that allow the actors to recount specific events and more general questions on the subject being investigated.
2. Questions should address specific situation of the actors’ experiences
3. The interview should be open enough to allow actors choose episodes that they found to be most interesting and relevant to them.
The table 1, below shows a summary of the data collection plan.
Table 1 Data collection plan
Node How actors were followed The actors followed
University LAN
Questionnaire
Semi-structured Interviews Activity logs
Design briefs Students
Web 2.0 technologies
Design studio
Activity logs Screen capture Archives
Students
Web 2.0 technologies Design inscriptions
Web 2.0 discussion spaces
Discussion forum archives Inscription observation schedule
Students
Web 2.0 technologies Design inscriptions
Drawing from Flick’s (1997) steps of interweaving, I devised the following strategy which I used to collect the relevant data from the actors:
1. Prepare for the interviews ensuring that I had all I needed to record and document the interviews and getting them to sign the consent forms.
2. Introduced my own view of the subject of my research to the participants
3. Obtaining the views participants’ conception of the subject of my interview and their biography in relation to the subject.
4. Establish the participants’ interpretation of the subject of my study.
5. Ensuring focus on the key issues of my study.
6. Asking some general issues related to the subject of my study.
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8. Documenting the interviews.
I requested eight students, who assumed the position of the group spokespersons, to participate in the interviews. All the interviews were made in person and recorded using a digital voice recorder with participants’ permission. Each participant completed a consent form to indicate their willingness to enter the study. However, to complement the recorded interviews I also took some notes. The duration of each interview ranged from 20 to approximately 40 minutes, depending on how many follow-up questions we asked. As noted in the section above, the questions used within the interview script were largely influenced by the stages of translation of ANT. I used the stories I generated from these interviews to trace the actions of the actors in the Web 2.0-facilitated collaborative design process as well as mapping the associations they described.
In order to collect data from the online discussion I asked permission from students to join their discussion as a guest. This meant that I was not going to interfere with the discussions of the students; however, I was able to collect all the discussions as they took place online. My task was to record the data and refrain from influencing what participants shared, thus giving the data the opportunity to tell the story. The actors led the direction of the research, which includes both human and non-human actors, to describe the design network since the task of my strategy was just to follow the actors.
It is noted that digital data collection spaces such as social networks, wikis, blog postings, and online forum discussions are often in a state of continuous flux, revision, and transformation (Adams & Thompson, 2014). Data collection from these places raises questions about what exactly constitutes data, or in Latour’s (2005) words, what is happening downstream. ANT’s answer is that everything that takes place is data. My data collection involved engaging various strategies and software to freeze and capture discussion postings into pdfs and sometimes taking screen captures, copying and pasting text or images into a word processing program.
Most of these methods involved ‘freezing’ particular moments of the online life-world.
Kallanikos, Aaltonen, and Marton (2010), give some words of caution that the snapshots produced by these methods are pages that have been temporarily assembled and presented in a particular space and time, and thus are no longer dynamic sets of information but have been rendered static with their links to the original source disconnected. Such digital artefacts are no longer mobile but frozen, decontextualised ‘photos’ (Kallanikos et al., 2010). Nevertheless,
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these freezing practices enabled me to cope with the volume of information that was continually propagated by the Web 2.0 tools. In other words, in ANT terms, through these frequent acts of freezing digital objects, I was together with the technology tools engaged in the translation of digital artefacts and data, excluding and including some along the way.
Furthermore, these data captures were also enacting what Law (2009b) refers to as a barely noticed collateral reality and boundary making. Kallanikos et al. (2010, p. 7), suggest that these practices of archiving data do not simply collect already bounded entities but rather “construct the boundaries that demarcate and make an archival document”. Once generated, captured and saved, these digital data become available for further translations and mobilisations through data analysis.
In an effort to bring Web 2.0 technologies to the level of actors in the collaborative design actor network, I treated them as key research participants in this study. Just as I collected data from students, I also collected data from various Web 2.0 technologies, which included Facebook, Twitter, WhatsApp, and LinkedIn as research participants in their own right. At first sight this idea seemed to be a bit off, but after a closer look it appeared less inexplicable. It is common knowledge that in the context of organisations such as universities the organisational sub-units, like faculties, departments, workshops, design studios and computer laboratories, can indeed assume some form of a life of their own. The same can be said about Web 2.0 technologies in a learning institution or when they are used in a learning context like a collaborative design project – they assume a life of their own and cease to be mere passive products. Since ANT considers the non-human elements of a network as equal participants with the human actors, the Web 2.0 technologies used by the participants constituted actors which needed to be followed, collecting the traces they left through the entire collaborative design process.
However, following the digital actors was not easy. To accomplish this I employed a variety of heuristics or ‘tricks’ to make the digital materialities of the network objects talk, “that is, to offer descriptions of themselves, to produce scripts of what they are making others-humans or non-humans, do” (Latour, 2005, p. 79). To collect data from Web 2.0 I made use of Adams and Thompson (2011), heuristics for interviewing non-human or “thingly” research participants. Digital actors in all their forms, including software, devices, networks, and artefacts, by their nature are notoriously fickle. Internet-based technologies in particular always exist as a flowing, indissoluble weaving of human-technology actors. They are often described
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as unbounded, evasive, distributed, and constantly mutating (Kallanikos et al., 2010). For example, the Web appears to us as a process, happening continuously and continuously transforming before our eyes, with us and through us. However, although the websites that are built bloom and disappear, the Web continues, because the Web is made up of people and the digital technologies of the Internet (Thompson, 2014). Thus, Ruppert, Law, and Savage (2013, p. 24) observe that “digital devices and the data they generate are both the material of social lives and form part of many of the apparatuses for knowing those lives”. However, in order to make sure that I did not lose any data, backing up on external storage was a daily practice.