# Cooperative Approaches to Data Sharing and Analysis for Industrial Internet of Things Ecosystems
## 3 Research Design and Methodology  
The two central research questions are aimed at identifying the role and the relevant building blocks for a novel type of organization: a data cooperative that supports SME ecosystems. The novelty concerns several core aspects of our research: the type of organization, a data cooperative; the configuration of an IIoT ecosystem that is grouped around a central cooperative; the aspect that the members are SMEs that individually mostly lack capital, experience, and analytical resources for extensive analytical initiatives; the aspect of data sharing across enterprise borders for analytical purposes (which is rarely seen outside a few niche scenarios); and the related aspects of governance, acceptance, and accounting in an ecosystem formed by equal SME partners. We conclude that we cannot draw the answers from literature alone. A quantitative study is not possible either, as we cannot even fully understand the relevant aspects to be included in a questionnaire. Our research is, therefore, explorative by definition, and we need to identify the potentially relevant building blocks of a data cooperative, their dependencies, and their interplay, as well as the reasons that support or speak against their inclusion. As such, we strive for hypothesis generation rather than hypothesis testing, and our methodology, both for data gathering and data analysis, is based primarily on the literature on qualitative research [52]. Following a common best practice in qualitative research, we begin by deriving a conceptual framework that structures the areas of insight we are focusing on [53]. We see that the most relevant \"unknowns\" are _not_ primarily located in the areas of IIoT and analytics technology. While there are open questions on how IIoT analytics services are tailored for a data cooperative (e.g., regarding the degree of the federation, the data models, the inclusion of big data components, etc. ), we already know the relevant building blocks and have a schema for specifying them (cf. Section 2.2). The largest knowledge gaps remain in the successful organization of a cooperative, division of roles of the cooperative and surrounding ecosystem, aspects of IT and data governance, accounting and pricing, and in aspects of organization and acceptance for sharing analytical data. Combined with the areas of novelty, this leads to the conceptual framework depicted in Figure 4. This framework guided the design of our interview questionnaire and the analysis of our results. As there is no existing sample of data cooperatives, we decided to collect data from two unconnected samples with different, complementary perspectives: interview series 1, with existing business networks, and interview series 2, with experts in data sharing and analysis. The first series is concerned with the cooperative approach. For the respective insight, we conducted 14 semi-structured interviews with representatives of German cooperatives from a variety of industries (cf. Table 1, left column). The smallest cooperative we interviewed (CO14) had 12 members, whereas the largest cooperative (CO11) had 43,960 listed members; the mean member number was 13,038; thus, our sample included small, medium, and large cooperatives. Although these are not _data_ cooperatives, there is a large area of overlap with the issues they face, particularly with respect to general questions about the organization of a cooperative, questions on how to deal with resource sharing and pricing, and governing a business ecosystem. Some interviewees also brought in experiences with the provision of general IT services and related questions on IT governance. For data sharing and joint analysis, we interviewed eight members of IIoT projects (with two exceptions all coming from the IIC) that all involved data sharing and data analysis across several enterprises (interview series 2). Unlike in the first interview series, the organizations in the second were mostly large companies [54; 55]. The average interview duration was approximately 70 min.