FROM INCUBATORS TO ECOSYSTEMS: EVALUATING THE STARTUP DIGITAL ECONOMY CLUSTER OF HULL CITY

Philosophical and ethnographic considerations for research

In the past years the number of researchers engaging in qualitative methodology in the context of management has increased (Boje, 2001; Smith 2001). However, it is clear the leading predominance to positivist science looking at the index of any general methodology manual for business researchers. Qualitative research deserve acceptance among the research community, and we are seeing some examples of qualitative research in management (see Van Maanen, 1998; Symon et al., 2002; Goulding, 2002). It is not easy to escape controversy and one always find over justifying methodological choices in order to legitimate the outcome of qualitative research. To give some examples of the hostility towards qualitative research, Denzin and Lincoln (1998:7) claim that the work of qualitative researchers generally has been called ‘....unscientific or only exploratory or entirely personal and full of bias…’. As a result, some researchers may be confronted by the question: why has been qualitative research being the target of this accusations? (Gill and Johnson 2010:147).

As it was mentioned above, how knowledge is structured in general methodology manuals for business researchers tends to lead to preferences towards positivist approaches, at least in terms of the quantity and quality of the information about methodology choices. Another observation derived from the information available from general methodology guides for business researchers also tends to embrace a systematic binary approach towards what somehow collides with any attempt to conduct research from a holistic perspective, as many experienced researchers in the field recommend. Of course, positivism has not escaped many criticisms, even from business researchers, being the main criticism the fact that a “high structured research design impose constraints on the results and may ignore other relevant findings” (Collis and Hussey, 2014:45).  

Ethnography is a methodology derived from anthropology (the study of people, their society, and their customs) in which the researcher uses socially acquired and shared knowledge to understand the observed patterns of human activity (Collis and Hussey, 2014:65). Ethnography “provides insights about a group of people and offer us an opportunity to see and understand their world” (Boyle, 1994:183).  The aim of the ethnography is to interpret the social world as the same way as the members of the particular world do. The main features of ethnographic research include 1) Participant observation, 2) the research taking place in a particular setting (both characteristics of ethnography will to be adapted and reframed under the complexity theory approach).

According to Creswell (2007:73), the entire culture-sharing group in ethnography may be considered a case, but the intent in ethnography is to determine how the culture works rather than to understand an issue or problem using the case as a specific illustration. In this particular research project, the issue or problem to understand is precisely the complexity in determining how a particular business culture works - specifically the business culture associated with entrepreneurial companies within the Digital Economy Hub of the city of Hull. The case study will pick methodologies and process frequently linked to ethnography and grounded theory both to interrogate issues related to 1) how the business culture works and 2) to code and analyze the results according to business model research and complexity theory standards.

In terms of data analysis, grounded theory would be also considered to be influencing the case study methodology choices. Strauss and Corbin defined grounded theory as an approach “that was derived from data systematically gathered and analyzed through the research process” (Strauss and Corbin 1998:12). In the present research project, data will be collected and organized by the researcher through coding (a common feature of grounded theory data analysis) in order to conceptually categorize variations in the data in terms of observed patterns and also to create indicators for a particular phenomenon (Gill and Johnson 2010:57).

This page has paths: