The philosophical assumptions of this study took me to the next step of the Map of collecting appropriate to the study and research question empirical data.
This process was driven by the qualitative methodology. Qualitative research methods are appropriate in the case of studying scaling of digital ventures, as there is limited existing understanding of the ways digital ventures scale, particularly across regional boundaries. With the aim of this research being to explore the use of replication in the scaling process, my motivations behind using qualitative methods was to gain a holistic overview of scaling of digital ventures. I wanted to understand the underlying mechanisms behind scaling that might be facilitated or made possible through digital, but at the same time affected by other ongoing strategies that cannot be attributed or explained merely through the affordances of digital technology. Qualitative methods gave me the right tools to explore a digital venture from inside and outside as I switched between being an outside observer and an involved researcher (Walsham 1995). In the next few sections I outline the way this was possible through two data collection phases and a distinct period of time in which I did not work on data analysis in order to distance myself from the case and regain a sense of reflexivity over data and the digital venture studied.
I decided to follow the case study method using three data sources, or as O’Gorman and MacIntosh’s (2015) refer to them research techniques:
interviews, observations and archival data linked to a single case of a successful digital venture in order to gain a deep understanding of the mechanisms at play that create rapid scaling of the user base of digital ventures.
3.2.1. Case Selection
For this study I selected BlaBlaCar out 4 other shortlisted digital ventures.
BlaBlaCar was a very appropriate extreme case (Gerring 2007) of rapid scaling venture in an international context. Extreme cases according to Gerring (2007)
“are paradigmatic of some phenomenon of interest” (p.101), focused on the variables that the research zooms in on, and therefore are a great tool for building theory. Other ventures that were considered included Waze (navigation), HealthTap (healthcare) and NearPod (eLearning), under the initial intention to study the development and scaling of a new digital practice, rather than scaling across regional boundaries. As the focus of the research narrowed, BlaBlaCar became the most suitable case for this study. BlaBlaCar represents a new breed of digital ventures with similar growth trajectories, such as the previously mentioned Airbnb and Uber. This makes the research relevant, generating findings and theory that can help explain the user base scaling mechanisms of other digital ventures. BlaBlaCar is different to many other digital ventures that scaled internationally at a similar rate. Other similar success stories first scale in a relatively homogenous US market. BlaBlaCar, on the other hand, incepted in Europe and scaled across 19 European states (and 3 non-European markets:
India, Mexico, and Brazil) with different languages, cultures, legal and financial systems. Scaling in such varied market conditions creates more challenges in adopting the product and service, setting up a local team, and generally speaking, conducting business. It requires really rapid rates of familiarising oneself with the local specificities of a given market. For this reason, the case of BlaBlaCar was the best possible option for exploring rapid scaling of the user base across multiple regional markets. The company was aggressively investing in its digital marketing and many members of the public as well as other research audience would have heard about BlaBlaCar, making the study applicable, accessible, and interesting within and outside of the academic IS circles. Further to this, I gained access to the company and was presented with an opportunity to collect rich primary data, which I supplemented with secondary data, spanning data analysis across three sources: interviews, archival data and participant observation.
3.2.2. Ethical Considerations
The research explored the digital venture from various angles, building on insights and documents with various levels of sensitivity and strategic importance. In order ensure that this information was handled ethically and was disclosed in the most unbiased way I took the following steps to adhere to the University ethical considerations. Each participant was sent a number of research documents: a research brief document that outlined the main purpose, aims and agendas of the research projects. Participants did not receive interview questions ahead of the interview, so the research brief had to outline the key themes that may be explored in the course of an interview. The interview brief was split into four main themes. This decision was made to leave space for exploring scaling in a semi-structured way, allowing for the participants to explore the most important, timely, and, relevant concepts to scaling as they see it in their role, team, market context, and in a given timeframe. Supplementing the research brief, I also used a research consent form, adapted and based on the University consent form with the University and Supervisor contact details, following the main ethical considerations and offering participants the flexibility and avenues to explore the research and procedures in more details, should they wish to do so. Participants signed the consent forms which allowed me to use the information disclosed for the propose of conducting this research.
Participants also had an opportunity to ask questions before and after the interview. I sent regular updates on the status of the research to the student liaison and a few other members of the team that were interested in the final research outcomes. This way I also maintained a link to the digital venture studied for any follow ups and ensuring that research reflected the processes of scaling in a most accurate and yet sensitive manner. Where interviews were fully transcribed, they were shared with the participants in order to avoid revealing any sensitive content or information that might have been exposed in the interview unintentionally. Participant names and roles have all been omitted form the transcripts, including the company name, and any other identifiers, such as names of events, or terms that in parts or fully included the name of the
company. Each interview participant was given a unique code that allowed me to identify their belonging to a function, a regional or a central team, which helped to structure data in the analysis stage. These identifiers were used for internal research purposes and when interview quotes were used in text, they were attributed to a manager from a particular team, with no regional identifiers.
All data, recordings, transcripts, and consent forms were stored on my personal computer and hard copies of transcripts or consent forms were stored and locked in the University Doctoral offices.