## 4 Findings
### Scenario Field Analysis and Scenario Prognostics  
The open questions resulted in a total of 90 positive and 91 negative effects. Overall, 46 of the study participants specified a \"most extreme positive effect\", while 44 participants specified a \"most extreme negative effect\". The outcomes were initially compiled into semantically identical factors (e.g., \"quick payment\" and \"fast transactions\" or \"high energy use\" and \"huge energy consumption\") and further grouped into overarching categories. The most suitable first-layer categories turned out to be the stakeholders' levels (individual, organization, and nation state), which were supplemented by one category for general issues. Within the first-layer categories, subcategories (S) were created according to the impact and/or perception of the respective effects. When it comes to individual perceptions, the emerging categories were related to transactions, security issues, and value. On an organizational level, the categories covered issues pertaining to transactions, security issues, and business aspects. On the national level, economic structures and environmental issues emerged as the main categories. The final results of this clustering and categorization process are shown in Table 2.  
From these positive and negative effects, the reciprocal influences of the factors can be assessed using a scale ranging from 0 (\"no impact\") to 3 (\"strong impact\"). This was mainly  
\begin{table}
\begin{tabular}{l l} \hline \hline Age & 25.08 \\ Gender & 63\% male, 31\% female (rest: N/A) \\ Bitcoin Owners & 23\% \\ Knowledge (0–10) & 5.27 \\ General Attitude (0–10) & 6.61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample characteristics (\(n=51\)).  
applied on the subcategory level (e.g., \"How important are secure organizational structures for national economic structures?\"); however, in some cases, a more fine-grained analysis was needed (e.g., \"How can a proliferation of illicit activities impact organization Bitcoin acceptance?\"). The goal of these analyses was to identify coherent bundles of factors, which can either foster or inhibit the future development of Bitcoin and the prediction of their future impact. A panel of seven experts with a substantial understanding of blockchain technology performed the analysis, and the results can be found in Table A1 (positive cross-impact matrix) and Table A2 (negative cross-impact matrix) in the Appendix B.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Subcategory** & **Positive Features** & **Negative Features** \\ \hline Individual (I) & S1. Transaction-related & Cheap & Expensive \\  & & Fast Reliable & Slow \\  & & Traceable & Traceable \\  & & & Lack of acceptance \\  & & & Complexity \\  & & & Unsecure \\  & & Vulnerable \\  & & Privacy/anonymity & Privacy/anonymity \\  & & Tamper-proof & Not traceable & Not traceable \\ \hline \hline \end{tabular}
\end{table}
Table 2: Influencing factors (properties) on different levels.  
Figure 2: Histograms illustrating the evaluation of Bitcoin: (**a**) medium of exchange; (**b**) store of value; (**c**) trustworthiness; (**d**) riskiness (\(n=51\)).