# Minimum Viable Products for Internet of Things Applications: Common Pitfalls and Practices
## 3 Research Methodology
### Data Collection and Analysis  
Figure 3 summarizes our approach to data collection and analysis. The major data source was semi-structured interviews. Semi-structured interviews are a common approach to collecting relevant insights into many phenomena in software engineering [37; 39]. The advantage of semi-structured interviews is that it allows improvisation and exploration of the objects studied [37]. Secondary data sources were published information about start-ups collected from the Internet. Prior to conducting interviews, we obtained as much information as possible regarding the start-ups' products, markets, customers and business models. As shown in Table 1, we conducted a total of twelve interviews with interviewees from eight companies. All interviewees are CEOs, CTOs or hardware engineers that have been with the start-ups from the beginning. The years of technical experience of the interviewees ranged from two to more than 10 years. The interview guide consisted of five sections: (1) business  
background, (2) idea visualization and prototyping, (3) product development, (4) challenges and lessons learned and (5) adoption of development paradigms. The interview guide is presented in Appendix A. During interviews, we adjusted the order of questions based on the responses and comments of the interviewees. Data were first collected from Case A and Case C to provide an initial basis for the development of the research questions. In these cases, the first author conducted follow-up interviews with both the CEO and the CTO. Due to the personal relationship of the author with the companies, we had access to other documents, including business model canvases, business plans and development contracts. Six months later, we collected data from Case D and Case E. This was done collectively by the first author. In these cases, we conducted follow-up interviews, asking participants to look at a list of Agile practices and discuss challenges and opportunities these practices present in connection to hardware development. After one year, we collected three more cases, Case F, Case G and Case H, with interviews conducted by the second author. Data for the final case, Case B, was collected by interviews conducted by two master's students from the Norwegian University of Science and Technology (NTNU). All interviews were conducted face-to-face and with the participation of co-authors of this study. \begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Id** & **Profile of Interviewee(s)** & **\# of Interviews** & **Avg. Years of Exp.** & **Other Documents** \\ \hline
**Case A** & CEO, CTO, hardware developer & 3 & 2 & Yes \\ \hline
**Case B** & CTO & 1 & 2 & Yes \\ \hline
**Case C** & CTO & 2 & 10+ & Yes \\ \hline
**Case D** & CEO & 1 & 7+ & No \\ \hline
**Case E** & CEO & 1 & 5 & No \\ \hline
**Case F** & CEO, CTO & 2 & 3 & Yes \\ \hline
**Case G** & Hardware developer & 1 & 3 & No \\ \hline
**Case H** & CEO & 1 & 4 & No \\ \hline
**Total** & & 12 & & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of qualitative data. Figure 3: Research approach. We adopted two approaches for analysis of the data: narrative and thematic analysis. To address RQ1, a narrative summary of interview transcripts and online documents relating to the cases was created to tell a story about each case [40]. The first and second authors interpreted and described stories of how start-ups prototype. To address RQ2, we adopted a thematic analysis, which is a common data analysis approach for qualitative data [41]. Thematic analysis is defined as \"a method for identifying, analysing and reporting patterns (themes) within data\" [41]. We applied a process of thematic synthesis, which has been applied and published in software engineering research venues [42]:  
1. Initial reading: We read through the transcribed interviews to generate initial ideas and identify possible patterns in the data. All interviews were transcribed shortly after they were conducted to ensure the actual meaning of interviewees' answers was preserved. 2.