4. METHODOLOGY
4.6 Method selection for field research
4.6.2 Research stage two
4.6.2.1 Case study research
As already described above, qualitative research is used to investigate concepts that are not well understood or researched in order to do rich exploratory research (Stebbins, 2001). Several different methods within the case were used to triangulate data but also to cover the complexity of the research
and the research question. Rich data also supports to validate and generalise findings when data reaches the saturation point by repetition (Glaser and Straus, 2010; Eisenhardt and Graebner, 2007).
As explained in the multi-level analysis section, the selection of methods also needed to ensure that data, with a focus on characteristics and actions of the agents, was collected for all levels of analysis to sufficiently describe the context the agents act in.
As the unit of analysis is the company and the individuals acting as particular agents, it was important to reach a context variety in order to find similarities in characteristics and actions that can be related to the research questions and that repeat themselves even when the context changes. That is why the context of research needed to display a variety of criteria when it came down to the case selection.
Different cases displayed different contexts and therefore were expected to validate repeating findings (Eisenhardt and Graebner, 2007). This means that a multiple case study of two opposing cases was the most appropriate way to validate repeating data. Furthermore, aspects that were discovered in the expert interviews about BE structures and network structures, as well as actions and characteristics related to the agents, also needed to be taken into consideration.
Another aspect was important to ensure the correct selection of the company, being an agent in network structures. Triangulation became very important here, as the individual company or agent could not be accessed solely by the studying of certain company criteria or secondary data in this research context, it was important to find the right point of entry for the case (Yin, 2014). That meant that the selection of methods also needed to consider the necessity of data triangulation in order to validate the identification of agents in networks.
Furthermore, it was important to define what the case actually is (Yin, 2014). Even though the unit of analysis was the single company, the case was chosen to be on a network level for three main reasons:
• First, a BE cannot be precisely delineated (Butel, 2014) and is therefore not selectable as a case.
• Second, as KS takes places between the organisations and individuals on an inter-organisational level, the case was defined to be on a network level (Inkpen and Tsang, 2005).
• Third, selective actions of particular agents by definition can only be observed on a network level (Schatzki, 2011).
This considerations made it necessary to approach the network level and identify the structure of the cases investigated. Which meant that a method needed to be developed that ensured the researcher’s access to the network level, investigate the network structures as the company and individuals’
context and then identify the Keystone agent as person and company. Consequently, a network mapping method was developed to access and understand the specifics of the network, in addition to the traditional case study methods, such as interviews, observations and document analysis. All methods are discussed below in more detail.
4.6.2.2 Case Study selection
One central aspect of data collection is that appropriate cases for analysis are chosen (Yin, 2014). No ideal number of cases is given. It is the information richness of cases that should guide their selection (Patton, 1990; Eisenhardt, 1989a). The cases selected were applied as being instrumental and confirmatory. Instrumental means the understanding of new aspects in the cases themselves and confirmatory the confirmation of theoretical aspects mentioned in the literature review for theory development (Healy and Perry, 2000; Perry, 1998; Miles and Huberman, 1994).
As already stated above, a multiple case study analysis was chosen in order to ensure a certain replication of findings to increase the reliability and robustness of the findings (Yin, 2014).
This is why the cases were chosen to have opposite contextual characteristics. As there is no ideal number of cases and the replication and data saturation shows the researcher if generalisability is possible (Herriott and Firestone 1983; Eisenhardt, 1989a; Strauss and Corbin, 1990; Yin, 2014), the case study selection remained to be two cases only. Additionally, data saturation was reached by this selection, as realised during the research process.
Another possibility to reduce bias and increase generalisability of findings is to include the retrospective into real time cases (Leonard-Barton, 1990). This is done by considering the length of case study investigation and by the inclusion of retrospective questions in interview questions. Both can be supported by documentation and archival data (Eisenhardt and Graebner, 2007). The critical incidents technique (CIT) can help to find repeating data around certain incidents of development when critical incidents are specifically asked for (Hughes, 2007). This could also be incidents in the past that help to understand actions of certain agents. Especially when distinct sources repeat certain key events that happened in the past, this can be considered to be critical incidents of development (Chell, 1994).
Summarising the above, it was important to consider that cases needed to be chosen by their possible contribution to theory: “but although multiple cases are likely to result in better theory, theoretical sampling is more complicated. The choice is based less on the uniqueness of a given case, and more on the contribution to theory development within the set of cases” (Eisenhardt and Graebner, 2007, p.565).
The cases to be investigated were selected by criteria that were summarised from the findings of the literature review about KS in collaborative relations, BEs and agents in BEs. Interview findings helped to develop case selection criteria further. The developed selection criteria are displayed in Figure 4.5 and 4.6. Agents in networks were suspected to develop the same or different KS actions or characteristics depending on the environment of their network structures or BE structures.
Therefore two opposite cases were selected in which the agents act in differing contexts with environmental differences.
Figure 4.5: Selection criteria for main case studies
Additional to the main selection criteria 70 publications on BE were analysed by the industry they addressed. The allocation of the 70 publications is based on the structured literature data collection process in Annex A. The dimensions analysed are displayed below in Table 4.2.
Industry
None 23 Theoretical concept, literature review,
conceptual article High number of theoretical and
conceptual contributions
All of the industries that are researched from a BEs Various 5 No specific industry, mainly global change
and high velocity developments addressed
Information
technology 16 Software, computing and information technology. Importance of high
connectivity among agents, high variety of agents, importance of platform of interaction
Mobile industry 6 Importance of high connectivity among agents, high variety of agents, importance of platform of interaction
Retail 5 Importance of high connectivity among
agents Automotive
(Electric vehicle) 3 Importance of high connectivity among agents, importance of innovation Semiconductor 3 High variety of agents, dependency among
agents
Aerospace 2 Meet market dependencies
Pharmacy 2 React to industry developments
Marine 1 High variety of agents
Agri-food 1 Importance of collaboration for innovation Carbon Trading 1 Emerging industry development
Solar Energy 1 Emerging industry development
Academic sector 1 Importance of interconnection among agents
Table 4.2: Industries addressed in business ecosystem publications
Variety factors Case I Case II
The results in Table 4.2 show that only very little research has been taking place in the automotive industry and no research in the sports industry. As outlined in chapter five, these two industries were accessible for the research conducted. Consequently, the research gap can be addressed in these two industries. Additionally, publications focussing on the industries above were shaped by the selection criteria displayed in Figure 4.6. These criteria can also be met by the automotive and sports industry as indicated (indexed sources in Figure 4.6 can be found in Appendix E). Building on these considerations the two industries selected are considered to be appropriate for this research.
Figure 4.6: Selection criteria derived from literature in business ecosystems
Case selection criteria was then used in for data processing to address suitable cases in the Sports and Automotive industry as displayed in Table 4.2.
Within the selected cases the aim was to look at the Keystone agent in one BE determined by certain network structures and to discuss the context of agents in that particular ecosystem rather that in a collection of ecosystems, in order find overlapping Keystone specifics and differences.
Due to the research approach, the research question, the data triangulation and multilevel analysis, the sources used for the multiple case study analysis included the traditional methods of documentation, archival records, interviews as well as direct observation of network meetings.
Additionally, qualitative network mapping as a new method was developed to access each case on a network level and support the identification of the Keystone in combination with the other two
Selection criteria Automotive industry
(Sources: S1, 2017-S5, 2017) Sports industry
(Sources: ID1, 2017-ID4, 2017; ID3, 2014; ID5, 1995)
Industry developments/ high velocity
developments x x
High variety of actors x xx
Dependency among actors xx x
Importance of interconnection among
actors xx xx
Importance of innovation xx x
primary data collection methods interviews and observation. The following case study data collection methods were used:
- Network mapping - Interviews - Observation
- Secondary data analysis (Documentation, archival records)
In order to ensure the validity of data and the comprehensive approach to all levels of analysis, the following research strategy was developed for the case study. The research strategy shown in Table 4.3 is only related to primary data selection and includes the expert interviews of research stage one as well. It also considers the data processing and analysis aspects that are outlined in chapter five.
Table 4.4 below summarises the overall research methodology framework introduced in this chapter.
Level of
Observation All actors Informal network
Table 4.3: Research strategy covering all levels of analysis
Aspects of the research methodology framework Detailed aspects of the research methodology Approach taken to address phenomenon Inductive, qualitative approach to research/
phenomenological approach (Holden and Lynch, 2004).
Epistemological orientation Relativism/ constructivism
Ontological orientation Subjectivism
Method for data collection Qualitative methods: Semi-structured interviews
Method for data processing Qualitative data analysis: coding method into patterns of meanings (Saldaña, 2014)
Table 4.4: Research methodology framework
4.6.2.3 Network mapping
As already discussed above, the network level was at the case study level (Yin, 2014). In order to find two opposed cases and create a context variety, it was important to understand what network structures dominate in what case. Due to this variety it was ensured that the patterns of Keystone agents could be considered to be valid.
The network mapping tool was developed in relation to the idea of social network analysis (SNA).
But whereas SNA concentrates on the quantification of social network relations in order to create a holistic picture of the network (Otte and Rosseau, 2002), the network mapping used in this work provided a first insight into the relations of particular network agents. SNA enables the researcher to access networked structures such as nodes, which can be individual actors or companies for example, and ties, edges, or links (relationships or interactions) that connect them (Scott, 2017). Furthermore, SNA can measure the strength of the ties and the exact position of the nodes. Even though this quantification cannot help to identify a certain BE or network agent, as these agents are determined by certain characteristics (Iansiti and Levien, 2004a), the main tool to access data in SNA, a network matrix, was considered to help in this research. It was used to ask for network aspects that enabled a better understanding of the Keystone role. Table 4.5 shows the network mapping matrix that needed to be send out to all members of the case study network. Additionally, some questions were asked in
order to identify the Keystone role which was then triangulated by the interview and observation data.
A sample of a network mapping form is displayed in Appendix D.
Name of
Table 4.5: Network mapping matrix modified to access qualitative data
In order to conduct the interviews planned on individual level, the agent, being person and company, needed to be identified first. This was done by network mapping but also by observation of network meetings and interviews with other network agents to ensure the right basis for the unit of analysis.
All observations were protocolled, stored and coded accordingly.
As knowledge is shared by individuals and organisations (Nonaka, 1994) the individual was accessed first and then the company behind the individual. The characteristics introduced by Iansiti and Levien (2004a) outlined in Table 2.2, as well as the first findings deduced from the expert interviews, were used to identify the different agents. To ensure that the unit of analysis was addressed in-depth, a focus on the interviews with the individual agent was important. The individual person and the company needed to be investigated in regards to the actions and characteristics that enabled them to fulfil their role and share knowledge within the network structures. In order to address agent specific characteristics and actions, other network agents were accessed by semi-structured interviews.
Additionally, open and semi-structured interviews with the individuals of the Keystone company were held to learn about their characteristics and actions that connect them with the agent company.
Furthermore, interviews were conducted with other employees of the agent company, selected by accessibility, to triangulate the statements of the individual agent. Here, it was essential that the employees chosen worked closely with the Keystone individual and were employed for more than one year at the Keystone company. It was furthermore important that a range of interviews were
conducted to identify the agent individual and company and to address many levels of firm activities that are important for strategy making (Mintzberg, 1978).
4.6.2.4 Observation
As observation is an important method to gain deep insight into people’s behaviour and action, for this thesis it was a key method to explore the Keystone role in CR. As it was important to not influence agents, the non-participating but direct observation method was chosen. As knowledge is also shared to a great extent by individuals (Nonaka, 1994), and agents are represented by individuals in networks (Brass et al., 2014), the individual behaviour was considered to be a key element here. An observation frame was developed that inherits the following aspects:
- Identification of Keystone person and company by Iansiti and Levien’s (2004a) taxonomies and by expert interview findings
- Personal characteristics of the agent individuals - Company characteristics of the agent company - Actions relevant for KS
- Actions relevant for strategy development in the agent company.
Observation as a method is very helpful in combination with interviews, as a rich understanding of the case, as well as the agent company and individual person, can be obtained. Due to the direct observation method an in-depth understanding of the dynamics between the interacting individuals is possible (Saunders, Lewis and Thornhill, 2012). Figure 4.7 shows the different research methods addressing the multilevel approach. It also shows how the different research methods can contribute to the different aspects of the main research question.
Figure 4.7: Different methods contributing to research question and multilevel perspective
4.6.2.5 Focus of case study: Keystone agents in network structures
During preparation of expert interviews and case study data selection process, it became obvious that a concentration on the Keystone agent, being one of the central agents within the network or BE structures, was a good choice as it enabled a focused case study research. Furthermore, findings related to the Keystone agent were also expected to reveal knowledge related to other agent roles as the Keystone does not act in isolation. Especially as the Keystone agent is supposed to provide a platform of interaction (Scaringella and Radziwon, 2017) for other agents, an analysis of the Keystone agent does prospect an insight into other agent’s behaviour. Due to the concentration on the Keystone agent, an in-depth analysis of expert interview and case study findings could be provided. As BE studies describe the Keystone as being the critical agent that keeps the ecosystem alive, that manages the exchange platform and that influences dynamics (Iansiti and Levien, 2004a; Mäkinen and
Expert interview Network mapping Case study interviews Case study observation Business
Data collection aims - Agent company characteristics - Agent identification - Agent relational and
Data collection aims - Individual characteristics - Agent identification - Agent relational and
Dedehayir, 2012; Rong et al., 2010; Rong and Shi, 2015; Den Hartigh, Tol and Visscher, 2006; Isckia, 2009; Quaadgras, 2005), the focus on the Keystone agent was kept. Expert interviews and observation in the case studies were aimed to support that decision, as it was expected to get the most data from this agent type suspected to be the main influencer of network dynamics.
4.7 Summary
Beside research philosophy and methods, this chapter provided an insight into the research strategy, design and methods as well as research ethics. As this work is based on an exploratory and qualitative approach, a detailed and comprehensive overview on philosophical matters, data collection methods, and distinct research stages has been outlined in this chapter. Furthermore the research process was outlined in order to provide a frame for the actual data collection and processing steps outlined in the next chapter.