DATA COLLECTION AND ANALYSIS INFORMATION DRIVEN INNOVATIONS

This research used a qualitative approach that can be studied using qualitative data. It is impossible to measure the research framework through quantitative methods. The research framework was applied (chapter 3), when the information driven innovations were studied. The goal was to create a good understanding of the BMC and key activities regarding data. This paragraph explains how data was collected and analysed for the desk and field research cases. In addition, reliability and validity is discussed.

48 Desk research

Cases were studied with a lot of public available information, such as company websites, annual reports, privacy policies, news articles, and/or company presentations. In addition, privacy policies were an important source to understand the key activities regarding data. Data was also collected by using the products/services of the desk research cases to gain more accurate insights. Several screenshots were included in chapter 5. Multiple sources were used to reach the saturation level and for triangulation, ‘to ensure that the data are telling you what you think they are telling you (Saunders et al., 2009, p. 146).’

Data collection was reliable, since it is likely that the same data will be collected when observations are repeated by applying the research framework (Babbie, 2010). Studied desk research cases have a lot public available sources. Therefore, a lot sources were consulted for a thoughtful understanding of the cases. The aspects of the research framework were the main focus during the data collection: nine building blocks of the BMC and key activities regarding data. Data was collected in a structured way and, mainly thereby, reliable. Validity is another quality measurement of research (Babbie, 2010). Validity is ‘a term describing a measure that accurately reflects the concept it is intended to measure (Babbie, 2010, p. 153).’ This research aimed to study how information driven innovations work. To study this, the BMC and key activities regarding data were sufficient and valid. Data analysis was also reliable and valid, since collected data was linked to elements of the research framework. During internal meetings and presentations with/for supervisors and (business model) consultants of InnoValor with academic backgrounds, the analyses were discussed in order to improve the validity of the cases analyses.

Field research

Before starting the interviews, initial BMC’s and key activities regarding data of the cases were studied to create a better understanding of the cases. Data was collected by studying the same sources as the desk research cases. There was not much public data available of the selected cases that were studied through field research. The interviews contributed to triangulate the data and to reach the saturation level. In addition, data was collected by using the products/services of the following cases: Coosto, Sense Health and TomTom Traffic.

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Five semi-structured interviews were completed in which a list of themes and questions were covered. The list of themes and questions are listed in appendix B. These questions were mainly open-ended and focused on the BMC and key activities. The questions to design the BMC were mainly developed by Osterwalder and Pigneur (2010). The questions about the key activities regarding data focused on the key activities in the DDBM. During the interviews, the BMC was composed together with the interviewees. Afterwards, to reduce interviewer bias, the initial BMC was discussed regarding differences and, thereby, strengthen the BMC.

Interviewees were representatives at management level of the information driven innovations (the unit of observation) who were capable to provide relevant input for this research. For example, a business development manager, managing director(s) and privacy and security manager were interviewed. Five interviews were completed with an average duration of 75 minutes. It is impossible in the scope of and irrelevant in the context of this research to explain every aspect of the BMC and key activities regarding data in detail in this report.

The role and performance of the interviewer and interviewee were critical to collect valuable information, develop relationships and prevent data from interviewer and interviewee bias (Saunders et al., 2009). This affected the reliability, since it depended on the interviews if other researchers collect the same data in repeated observations. Familiarity with the questionnaire and subject, following questions wording exactly, recording responses exactly and probing for responses were important guidelines that were applied during the interviews (Babbie, 2010). As a result, this benefitted the reliability and validity of the data collection.

Data analysis was also reliable and valid, since answers of interviewees was linked to elements of the research framework. This was possible, since questions were asked that were directly linked to the BMC and key activities regarding data (appendix B). This enhances the validity. Besides, the memoing technique was used: ‘writing memos that become part of the data for analysis in qualitative research (Babbie, 2010, p. 404).’ Therefore, it was not needed to use an extensive analysis methodology of qualitative data. In order to triangulate and optimise the data of the semi-structured interviews, extra public available data sources were consulted after the interviews. Privacy policies were an important source to understand the key activities regarding data.

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During internal meetings and presentations with/for supervisors and (business model) consultants of InnoValor with academic backgrounds, analyses were discussed in order to improve to improve the validity of the cases analyses. Finally, the results of the case analyses were sent to the interviewees to confirm the analysis. Thereby, the quality of the case analyses were guaranteed.

4.4. DERIVING INFORMATION DRIVEN BUSINESS MODEL PATTERNS