Research design

This thesis uses an experimental mixed-method approach to gather data on team creativity. Teams of three participants complete six creativity activities using the CreativeTeams tool (see section 4.2 for more details). These activities are completed in either a co-located or virtual environment.

Figure 3.1: My mixed method research design

The experiment generates both quantitative and qualitative data. The out- puts of four of the six creative activities can be scored to provide an indication of creative performance. Sessions are filmed to capture the teams verbal and physical (body language) interactions. Furthermore, the actual drawing process is recorded providing a video of the creative interactions during the activities.

Figure 3.1 illustrates this process with both quantitative data (creative perfor- mance scores) and qualitative data (filming, drawing processes) being captured concurrently during the experiment.

The scores generated are assessed along with meta data generated by the CreativeTeams tool to establish the difference in the creative performance of co-located and virtual teams. Qualitative analysis is then used to explore any sessions of note identified during the quantitative analysis stage.

Mixed method approaches synthesise quantitative (quan/QUAN) and quali- tative (qual/QUAN) methods to derive knowledge. Mixed methods is often rep- resented as the third research paradigm [146] or methodological movement [147]. Creswell and Plano Clark [148] suggest that the design of a mixed method ap- proach is informed by four key decisions:

1. The level of interaction between strands. They [148] suggest that strands are either independent, with no interaction until the interpretation phase; or,interactivewhen quantitative and qualitative strands mix at some stage prior to interpretation.

In the experimental approach used both quan and qual data are gathered concurrently. However, the two strands are analysed and interpreted sepa- rately. The quan strand interpretation informs the selection of aspects for analysis in the qual strand.

2. The relative priority of the strands. That is, are quantitative and qualitative methods of equal importance to the research. Priority tends to be shown by capitalising QUAL or QUAN to show emphasis. Both can be equal: QUAN + QUAL. Or emphasis can exist either way: QUAN + qual

or QUAL + quan.

In this study the QUAN strand is prioritised over the qual strand. This is because the psychometric tests that have been adapted are well estab- lished and extensively tested. This increases the reliability of the QUAN results. The qual strand meanwhile attempts to explore the underlying socio-cognitive processes. The design of the experiment attempts to stim- ulate these processes, however there is no guarantee that they are actually present.

3. The timing of the strands. This relates to the order of the research strands, with timing being either concurrent, sequential or multiphase. Both QUAN and qual data will be captured concurrently during the exper- iments. The QUAN analysis and interpretation will then take place. This will allow RQ1 to be addressed, it will also highlight any unexpected or contrary relationships. These will then be assessed using a qual approach. 4. The procedure for mixing the strands. This relates to where and how

quan and qual strands mix. It is suggested that strands can mix either during interpretation, analysis, data collection or at the level of design. QUAN AND qual strands follow a largely sequential pattern following the data capture phase. The QUAN strand will address RQ1. However, the interpretations of both QUAN and qual strands need to be combined to develop a richer more effective understanding of creativity.

Babones [149] suggests that quantitative approaches are readily compatible with interpretive approaches, noting that “key tenets of interpretive quantitative methodology are the triangulation of research results arrived at by analysing data from multiple perspectives, the integration of measurement and modelling into a more holistic process of discovery and the need to think reflexively about the manner in which data came into existence.” [149]. The research design outlined here triangulates the quantitative study of creativity by using TTCT, Guilford’s Alternative Uses test and the Design Challenge activity designed by myself (which I will explain in more detail in section 4.1) to explore creative performance from multiple perspectives. Furthermore, this research design sets out to supplement these findings with additional quantitative data gathered from video recordings of interactions and the drawing process themselves. These sources are combined during the analysis to explore the co-creation of drawings and test responses. The complexities and implications of these interactions is then reflected upon in section 7.4.

This research is not looking for causality but rather to explore if there are any differences between the two environments in the narrow context by which creativity is framed in this study. There is little apriori theorisation because

of the lack of previous related research to build upon. I assume that creativity is socially constructed and will remain so throughout this study. I purposefully combine quantitative and qualitative approaches to explore the tension that exists between performance and social co-creation. The use of a quantitative approach provides a structure from which to begin to address RQ1. It also provides a more refined context to focus the exploration of the underlying interactions that occur as the teams collaborate on these creative activities.