## 3 Materials and Methods
### Data Analysis  
Interview transcriptions were analyzed using the five steps prescribed by [PERSON] (2008). [PERSON] suggested identifying analytical categories (1) and coding guidelines (2) before coding. These steps will structure interviews without sacrificing the diversity of replies. Next, in the coding stage (3), interviews should be examined against the coding guide. This procedure allows researchers to retrieve statistics from the interview transcripts (4). The author recommends continuing the interpretation of interviews (5) in a less structured way with the aim of deriving hypotheses. Additionally, Notion, a data management software, was employed for the research ([PERSON] et al., 2017).  
After collecting all transcripts and the researchers' memo, the interview analysis was started. Firstly, rigorous iterative reading of transcription combined with literature review produced seven analytical categories:  
1. I4.0 awareness;
2. I4.0 implementation;
3. Critical factors;
4. Demanded I4.0 enablers;
5. Established partnerships;
6. Impact of I4.0;
7. Future perspectives of I4.0.  
The analytical categories reflected only the topic of a respondent statement. Thus, coding guidelines were developed to consider the actual content of phrases. Mostly, the coding guide consisted of 3-5 answer options, semantically equivalent to real answers. However, the demanded enablers guidelines were the 19 I4.0 technologies, principles, and practices mentioned by respondents. Moreover, the guide for future perspectives category was discarded because of all answers being unique. Additionally, peculiarities associated with the ongoing COVID-19 pandemic were noted. Respondents mentioned the difference and nuances of the digital transformation during the pandemic and its effect on some aspects.  
Afterwards, all authors independently manually coded all interviews. Interviews were evaluated using the coding guide. Combined results were thoroughly discussed and quantified. Furthermore, a comparative quantitative analysis of interviews was performed. Firstly, responses of ten user companies were compared against responses of five experts. The next comparison criterion was the size of the company. Replies of six large enterprises were compared to those of nine SMEs.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Interviewee ID** & **Role in I4.0** & **Company Size** & **Type of the Industry** & **Management Level of the Respondent** \\ \hline
1 & Provider & SME & Business Consulting & Senior \\
2 & Consultant & LE & Construction Industry & Middle \\
3 & Consultant & SME & Business Consulting & Senior \\
4 & Provider & SME & Consulting Engineering & Senior \\
5 & User & LE & Construction Industry & Senior \\
6 & User & SME & Electrical Engineering & Senior \\
7 & User & LE & Petroleum Industry & Middle \\
8 & User & SME & Telecommunications & Senior \\
9 & User & SME & Information Industry & Senior \\
10 & Provider & LE & Electrical Engineering & Senior \\
11 & User & SME & Financial Services & Junior \\  & & & Low-Tech & Middle \\
12 & User & SME & Manufacturing & Middle \\
13 & User & SME & Electrical Engineering & Junior \\
14 & User & LE & Professional Services & Middle \\
15 & User & LE & Mining Industry & Senior \\ \hline \end{tabular}
\end{table}
Table 2: Participants details of the experts’ panel.  
The text was further interpreted using qualitative content analysis procedures [60]. For each of the seven analytical categories, all the related data were retrieved from transcription. Causal links, common patterns, anomalies, and differences were identified using axial coding and then discussed [61]. Key factors and aspects were identified during the selective coding.