The entire premise of the book What is a Case? Exploring the Social Foundations of Inquiry derived from a symposium in 1988 at Northwestern University with the specific intention to ask, ‘what is a case?’ Defining what a case study is was shown to be a difficult task. Ragin states that:
Comparative social science has a ready-made, conventionalized answer to this question: Boundaries around places and time periods define cases.
(Ragin, 1992: 5)
Ragin continued, writing that the question remains: ‘What is it a case of?’ The approach, taken in the study of the Momentum grant programme, has been a case since the beginning, although the understanding of the boundaries of the case changed as the project developed. For the purpose of this research, the time-scale for the case of Momentum begins in May 2013, approximately at the time of the programme’s launch. As the Momentum programme remains active, a clear end point was difficult to decide, but the time-scale was decided to coincide with the available quantitative data at the time of the analysis. This includes the first
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through fourth years of the fund (2013-2016). There has since been a fifth year of Momentum (2017), and the sixth is underway (2018), but these will only be
considered insofar as to provide an up-to-date view of where the programme is now in this study. The data from the fifth-sixth years and any beyond are not included within the case study, partly due to time and resource limitations. The scope of the case study research includes:
1. The organisations involved and the individuals working on Momentum at these organisations, specifically the primary funder, Arts Council
England (ACE), and the additional funder and manager of the programme, the Performing Rights Society Foundation (PRSF);
2. The individual musicians, artist managers and small companies, such as record labels, seeking Momentum funding;
3. The external music industries experts called ‘advisors’ who are brought in to the programme by PRSF to act as decision-makers on the funding.
The aim of case study research is to provide a rich portrayal of a situation or event, yet studying a single case could be considered a limitation. However, when the case is exemplary there may not be any comparison point or similar case.
Stavraki highlights the strengths of the single case study, showing three ways interpretive single cases allow the researcher to capture rich context. Firstly, they
“provide thick descriptions that illuminate the actions, narratives and voices that shape individuals' experiences,” in keeping with Clifford Geertz’s established practice of ‘thick description’ in anthropology (Stavraki, 2014: 5). Secondly, they allow one to “capture the complexities of the phenomenon under investigation,”
which might be missed in a study with multiple cases. Thirdly, they “develop context-embedded accounts that reveal multiple voices” (Stavraki, 2014: 5).
Capturing and reflecting rich descriptions, complexity within the case and context is paramount to this study’s aims of understanding the dimensions of cultural value created by public funding for popular musicians. The ability to include the
complexity of multiple perspectives within one investigation was a prominent reason for selecting a single case. My study also adopts an embedded single case design, which includes analysis of ‘subunits’ within the case (Yin, 2018: 52/322).
To clarify through an example, the data from musicians that is gathered by PRSF
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in application forms or impact evaluations is used in my research as a survey of the subgroup of musicians within the case.
Despite the strengths of case studies, there are limitations with single case studies. For example, Yin (2018) cautions the researcher to be careful of single cases because of the inability to compare across other case study sites or activities. However, the potential value of the single case is its ability to deeply illustrate social phenomenon that is exemplary, or where the case can be justified as “an extreme or unusual circumstance” (Yin, 2018: 53/322). The Momentum case, I argue, is an unusual exemplary case, in that there has not been a publicly funded grant programme providing funds directly to popular musicians in England.
Momentum represents a departure, not only for ACE in supporting popular music with grants paid directly to popular musicians, but also as it is a bespoke fund that was established to meet a specific need identified by ACE, which had not
previously existed. In addition, single cases can act as testing grounds for further multi-site case study research, and this study proposes this is needed in the area of popular music and public funding. The next sections will describe the primary and secondary data gathered on Momentum and driven by the research
questions.
DATA SOURCES
The data included in this case study includes both primary and secondary data types. Table 3.1 below details these types of data, broken into primary and secondary data sources. The primary focus of the research has been the
perspectives of value in the Momentum fund. My approach to studying Momentum sought multiple perspectives from different actors and aimed to gather different types of data that would allow perspectives to be compared and data verified in a process of triangulation. I describe the details of the data collection later in the chapter.
The primary data sources that were consulted to answer the research questions included observations, ethnographic in-depth interviews and
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structured interviews with six different sources. These are seen in Table 3.1. The first source was observations of four Momentum funding assessment panels, where funding decisions are made for each round of Momentum. In each funding round, there are two separate panels divided by genres, where between 35-40 shortlisted applicants are discussed by four external advisors appointed by PRSF.
In the panels I observed, three members of PRSF staff were also present. In two panels, one representative from ACE was present. These panels lasted
approximately four hours each and provided invaluable insight into a funding process that, for many funding organisations, is usually obscured. I was interested in the ways value emerged during funding panels and was articulated and
contested by different actors during panels and decision-making. I was also able to observe an informal shortlisting process for one Momentum round, where two PRSF staff sorted applicants by their scores and discussed the factors that could assist certain applications move to the final panel stage. The observation allowed me to watch while some direction was provided, and I could ask questions.
In-depth ethnographic interviews formed a second source, and these were conducted with musicians funded by Momentum (row 2 in Table 3.1) and artist managers working with funded musicians (row 3 in Table 3.1). Eleven interviews were conducted, including one pilot interview, nine with Momentum-funded
musicians, one joint interview with two funded musicians and one interview with a do-it-yourself (DIY) musician who had not sought funding. These interviews lasted between nearly an hour to almost two hours. Fourteen in-depth interviews were also conducted with artist managers. As explained in the introduction and Chapter 6, ‘artist managers’ are defined in this thesis as any individuals working with popular musicians in a capacity where they handle multiple tasks typically carried out by artist managers, such as negotiating deals, coordinating workers and release plans, diary management and securing investment.
I sought organisational perspectives of value through semi-structured interviews with staff involved in Momentum at ACE, PRSF and one from Momentum’s funding partner, Spotify (rows 4-6 in Table 3.1). I interviewed individuals who had been involved in the origination, evolution and running of Momentum, speaking to two senior staff at ACE and two staff at PRSF. I chose to
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conduct semi-structured interviews as the time available for interviews was more limited, and I sought to pose similar questions to each interviewee to allow comparison. The primary data is central to the analysis of value from the four different perspectives presented in the thesis, with Chapter 4 focusing on ACE, Chapter 5 on PRSF, Chapter 6 on artist managers and Chapter 7 on funded popular musicians.
In addition to the qualitative primary data, I gathered and analysed
secondary data with the goal of gaining insight into ACE’s and PRSF’s aim’s for Momentum funding and facilitating triangulation, or verification, of other primary data with secondary data. The secondary data is listed in Table 3.1 in rows 7-9, showing that primarily data was gathered corresponding with the 1st - 15th rounds of Momentum, as time constraints required that I end the data collection whilst the 16th round was in progress. Ideally, in future research, data would be included through 2018 and beyond, as the funders contributing to Momentum changed several times6, creating distinctive phases within the funding. In some cases, documents were also gathered from the period prior to Momentum’s creation to understand the context. Documents were gathered if they were created by or about ACE, PRSF and Momentum. Both external evaluations created by
consultants were reviewed. I also sought data that covered as much of the funding process of Momentum, so I searched for all documents available as part of
Momentum, such as assessment criteria and guidance sent to assessment
advisors. By bringing together primary data and secondary data, I aimed to form a clearer picture of the entire process of assessment, from application to evaluation.
6 At the time of writing in 2018, the organisations funding Momentum have changed five times since it was launched in May 2013. Originally, ACE was the primary funder, with PRSF contributing and Deezer as digital partner. The first change came when Spotify replaced Deezer as the digital partner in 2014. Then PPL joined as a funder in November 2016, Creative Scotland and the Arts Council of Wales joined in 2018, and ACE ceased its funding contribution in 2018.
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Table 3.1 List of Primary and Secondary Data Sources
Quantitative programme data supplemented the qualitative and
documentary data, providing insight into the results of the programme and the ways it had changed over time in ways that observations and interviews could not.
This data was downloaded from PRSF’s funding system Flexigrant and exported to Excel. The different data sets of quantitative programme data analysed in this
Primary Data
Collection Method Number Time Length
1. Observations at PRSF Assessments
4 Assessment panels for 2 Momentum deadlines
• 9 with Momentum-funded musicians, including 1 joint interview with 2 musicians
• 1 with a DIY musician
Ranging from 56 mins to 1hr 52mins
3. Ethnographic Interviews with Artist Managers
14 in total Ranging from 25
minutes to 1hr 19 mins
4. Semi-Structured
1 Staff member Approx. 1 hour each
Secondary Data
Collection Method Number Time Frame
7. Document Search for Huffington Post and from staff
For period covering
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research are listed in Table 3.2. There are six ways to categorise the data available within the raw quantitative programme data available for Momentum applications. Table 3.2 lists these categories, including the time frame they cover and the number of applications in each category, showing the possible ways the data could have been divided. It is worth noting that, while the number of
applications is the total available sample for each category, different data points have varying samples, depending on how many applicants/funded artists provided answers to specific questions on the application or evaluation forms. I explain the processes I took to analyse the programme data in section 3.3, but I will now explain how participants were selected for this research.
Table 3.2 Quantitative Data from Momentum Application Forms by Category
Momentum Application Programme Data Categories and Samples
Data Source Time Frame Sample
1 All apps. (including reapplications) Rounds 1 – 7 (part of 8)
May 2013 – October 2014 1439
2 All apps. unique Rounds 1 – 7 (part of 8) May 2013 – October 2014 1319
3 Funded apps. Rounds 1 – 7 (part of 8) May 2013 – October 2014 61
4 All apps. (including reapplications) Rounds (part of 8) 9 – 16 (part of 17)
October 2014 – May 2017 1904
5 All apps. unique Rounds (part of 8) 9 – 16 (part of 17)
October 2014 – May 2017 1545
6 Funded apps. Rounds (part of 8) 9 – 16 October 2014 – April 2017
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SELECTION OF PARTICIPANTS
For the selection of primary sources and participants, this study employed an approach that is similar to purposive sampling and theoretical sampling. Purposive
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sampling was used for determining which Momentum-funded musicians to interview. This is because the research questions regarding musicians require variety in the sample (Bryman, 2008: 415; Punch, 2014: 162). The project is concerned with the different kinds of experiences musicians have with Momentum funding and the multitude of ways of valuing – no ‘kind’ of experience is elevated above another. Purposive sampling can help obtain perspectives of certain kinds of musicians – for example, non-London based musicians – who may be
underrepresented in the funding pool. Due to difficulties accessing musicians, because of non-responses and time constraints, a purposive sampling approach was helpful in attempting to target specific types of musicians that I had not interviewed yet. Purposive sampling was also used to select the key staff interviewed at ACE and PRSF and the Momentum assessment panels for observations. McCall (1984: 265 cited in Lee, 2000) makes the statement that
“observation is always selective and purposive” (1984: 270 cited in Lee, 2000: 44).
Researchers make decisions about factors including the subject of study, where it occurs and what time. I chose to focus on musicians who had received funding, and this was purposive. These decisions were all made within the case study framework and around the research questions identified in Chapter 2. It should be noted that purposive sampling can risk bias on the part of the researcher. I
attempted to mitigate this by also contacting funded musicians randomly. Relying on purposive sampling techniques was somewhat successful with musicians, but randomly contacting funded musicians was the most helpful in acquiring
participants. There are limitations to the data: there may have been individuals who were less visibly involved in Momentum at ACE or PRSF, who I was not able to speak to, and who cannot include their views. While I knew that the
serendipitous moments of data collection sometimes come from surprising places, I was limited in my ability to cast a wide net with musicians, as my professional networks were mostly within the funding world. This means that this research cannot include the perspectives of musicians whose managers applied to Momentum for them but were minimally or not involved in the funding process.
Musicians who applied many times and were never successful are also not reflected. Future research would benefit from greater input from musicians less involved in large urban music scenes, such as those living in more rural areas.
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Effort was made to include the greatest variety of funded musicians, though representation of all regions, genres, funding levels or ethnicities within
Momentum was not possible due to time limits and difficulty getting responses.
The spread of the data can be viewed in Appendix A.
Sampling was also considered when acquiring secondary data. Documents were selected for analysis primarily based on theoretical sampling, which
emphasises an on-going data collection and analysis process. Theoretical sampling is “the process of data collection for generating theory whereby the analyst jointly collects, codes and analyzes his data and decides what data to collect next and where to find them, in order to develop his theory as it emerges”
(Glaser and Strauss, 1967, cited in Bryman, 2008: 415). The collection of
documentation occurred throughout the phases and had no finite barriers. This is the theoretical sampling model. Selected documents spanned the Rounds 1-15, as these were the funding rounds that had occurred when data collection began to wrap up. Similarly, quantitative programme data was collected for Rounds 1-15, with 16 partially completed. It was decided that, as 16 was in progress, no
meaningful analysis could be comparatively made to the rounds that had already occurred. The research questions and theory relevant to this project (see Chapter 2) will initiate the first sample.
DATA COLLECTION: PHASES
The research project examined multiple actors through several overlapping
phases of data collection. Approximately five overlapping stages of data collection occurred within the 2015-2016 period (see Table 3.3 for a timeline). These phases were: 1) Observations at Momentum advisor assessment panels organised by PRSF and where funding recipients were determined; 2) semi-structured interviews with Momentum-funded musicians and artist managers working with Momentum musicians; 3) semi-structured interviews with key staff at PRSF, ACE and funding partner, Spotify; 4) collection of programme data and statistics for the Momentum programme and grantees; and 5) document collection and analysis.
Table 3.3 below provides details of the phases and their timelines.
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The five phases of collection ran in an overlapping fashion from November 2015-December 2016 until the data amassed seemed enough to “provide
confirmatory evidence (evidence from two or more different sources) for most of [the case study’s] main topics” (Yin, 2018: 112/322). I determined that enough material had been gathered from different sources – primary and secondary and qualitative and quantitative – to allow triangulation of analysis and data sources.
Table 3.3 Phases of Data Collection November 2015 – December 2016
Phase of Collection
Data Collection Focus Time Frame
Collected
1 Observations at Momentum advisor assessment panels organised by PRSF and where funding recipients were determined
2015 - 20167
2 In-depth interviews with Momentum-funded musicians and artist managers working with Momentum musicians
Jan. 2016 – Nov. 2016
3 Semi-structured interviews with key staff at PRSF, ACE and (funding partner) Spotify
Jul. 2016 – Oct.
2016 4 Collection of programme data and statistics for
the Momentum programme and grantees
Dec. 2016 (for 2013-2016 data) 5 Programme documentation collection Periodically
throughout late 2015 - 2016
7 The specific dates of the observations cannot be revealed in order to maintain anonymity of the artists discussed and the external advisors present.
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