Conducting audience research "in the wild" is a complex task with many uncontrol- lable variables like the types of performance, the sizes and types of venues and different populations of audience among others. In order to achieve the depth and detail required for this level of research, this thesis had to have a narrow scope in order to focus on the signals that audiences provide unconsciously to the dancers. The following section will reflect on the methodological limitations of the research carried out in this work in achieving the research questions provided in Chapter 1 and discuss some future direc- tions.
8.4.1 Measuring audiences and dancers responses "in the wild"
The first limitation occurs from the challenges that arise when collecting data from random audience samples in real theatrical settings. While this scenario provided un- biased data from audience members who chose to attend the specific performances, it made it difficult to acquire any information from the audience members before or after the performance (Study I and II). Therefore, the analysis was conducted with missing data from the participants such as information about their dominant hand, self-reported engagement data and other demographic information that could have been useful for the interpretation of the results. This was improved in the final study (Study III) which was conducted using recruited participants. This resulted in a more controlled study with pre and post performance surveys so as to be able to more accurately test specific hypotheses.
However, even though in the final study (Study III) a more controlled methodological approach with recruited participants and wearable devices was followed, due to venue restrictions, filming of the audience was not allowed. The lack of audience video recording made it difficult to interpret audience acceleration data. Video recordings would have
helped to observe what people were actually doing during these moments and use these to compare with the sensor data. Sensor data can provide accurate continuous responses but when used on its own the interpretation of the data is not always accurate. In order to be able to collect good quality audience data from a big enough sample size, in such an explanatory study like the one studied here, a combination of sensor data and video recordings is needed. This was not possible in this research due to budget limitations.
Overall, more participants and a combination of sensor and video data would have in- creased the statistical power of the analysis and boost confidence in any findings. Future work must involve a larger sample of audiences where their responses will be measured using a combination of video recordings and sensor data. While this methodological approach may be quite expensive to undertake, it will provide more accurate results.
Another limitation of this study that has to be considered is the lack of data collected from the performers. Since the research question of this thesis focuses on the bidirectional relationship between audience and dancers, collecting continuous or post performance data from the dancers would have been beneficial. However, this was not possible due to time and technical limitations.
To be more specific, in order to gain stronger evidence of what dancers are able to detect from the auditorium, it would have been beneficial if the dancers were interviewed straight after the end of their performance or possibly given a post-performance survey to fill in.
8.4.2 Scientific tools to measure fine-grained audience responses
In a broader picture, another issue that appeared after conducting this research re- lates to the difficulties faced during the analysis of human behavioural data that have been collected from real theatrical settings. This kind of analysis is an inherently multi- disciplinary problem and there is no existing method that can analyse social non-verbal interactions. There are no tools that can capture and analyse peoples’ social inter- actions, patterns between interacting individuals or the dynamics in an audience or a crowd. Non-verbal behaviours can be ambiguous and sometimes may not be associated to a specific meaning. Their appearance can depend on factors that have nothing to do with social behaviour. For example, postures correspond in general to social attitudes, but sometimes people need to make them to feel more comfortable. Moreover, the same signal can correspond to different social behavioural interpretations depending on the context.
One way to deal with this is to use multiple behavioural cues extracted from multiple modalities so that the problem can be approached from different aspects. Improved data analysis techniques with a focus on sophisticated digital signal processing algorithms will be beneficial and provide a more accurate analysis of audience and dancers data.
In general, audience research and the area of human to human interaction would benefit from the collaboration among people from different disciplines. For example, engineers must include social sciences in their reflection, while social scientists must formulate their findings in a form useful for engineers and their work.
Overall, the present work reveals the need for new scientific tools that will be able to measure fine-grained audience responses and make sense of those measures. Audience research needs to change direction and look in more detail at what is happening. Surveys and questionnaires should be used in a more efficient way but not used as the primary source of data for analysis. Performance unfolds in time and it is not efficient nor accurate to summarise a whole performance piece based on one number or one sentence.