## 4 Case Study
### Results
#### 4.2.2 Network Analysis
The criterion to divide value dimensions into core and associated values are expected to be further explored in future studies; judging whether a certain value is more important than another has no correct answer. The full semantic network is shown in Figure 3, along with the partial semantic network, which only shows nodes that have degree centralities of at least 15 and where eigenvector centralities are indicated by the size of each node. Note that this study used Ucinet and Gephi for applying network analysis. Figure 3: _Cont._  
Network Analysis of Convenience  
As convenience was selected as a core value dimension, a co-occurrence matrix consisting of the values associated with convenience was constructed. The matrix includes 56 co-occurrence rows. Using network analysis, node-level centralities were then quantified again (Table 2). As a result, nodes such as type of control, fatigue, gesture shape, practiced, desire to use, learnability, easy, fun, age, repetitive, and range of motion turned out to be important in terms of degree centrality. While type of control, fatigue, and gesture shape were also regarded as important in the overall network analysis, nodes such as practiced were regarded as relatively more important in the convenience analysis. The semantic network belonging to convenience is shown in Figure 4, where nodes with degree centralities of at least eight are shown and eigenvector centralities are indicated as node size. \begin{table}
\begin{tabular}{c c c c c} \hline
**Node** & **Degree * & **Betweenness * & **Eigenvector * & **Node Type** \\ \hline Type of control & 27 (1) & 120.7 (1) & 0.301 (1) & Design variable \\ Fatigue & 17 (2) & 37.8 (2) & 0.203 (2) & Value dimension ** \\ Gesture shape & 15 (3) & 36.1 (3) & 0.137 (11) & Design variable \\ Practiced & 13 (4) & 16 (4) & 0.176 (3) & Value dimension \\ Desire to use & 12 (5) & 13.8 (5) & 0.164 (4) & Value dimension \\ Learnability & 11 (6) & 6 (13) & 0.164 (4) & Value dimension \\ Easy & 11 (6) & 10.4 (7) & 0.159 (6) & Value dimension \\ Fun & 11 (6) & 6.9 (10) & 0.153 (7) & Value dimension ** \\ Age & 10 (9) & 7.3 (9) & 0.149 (8) & Value dimension \\ Repetitive & 10 (9) & 7.4 (8) & 0.122 (16) & Design variable \\ Range of motion & 10 (9) & 10.5 (6) & 0.12 (17) & Design variable \\ Doubtful & 9 (12) & 1.9 (15) & 0.142 (10) & Value dimension \\ Distrust & 8 (13) & 1.9 (16) & 0.143 (9) & Value dimension \\ Independent & 8 (13) & 6.3 (12) & 0.104 (21) & Value dimension \\ Quick & 8 (13) & 6.3 (11) & 0.101 (24) & Value dimension \\ \hline \end{tabular}  
* The numbers in parentheses represent ranks in the corresponding centrality. ** Core value dimensions.  
\end{table} 
Table 2: Centrality of representative nodes associated with convenience. Figure 3: Semantic networks of smart TV gesture interactions. (**a**) Co-occurrence network of the 132 significant keywords. (**b**) Semantic network of representative nodes (red and blue nodes indicate core values and design variables, respectively). As fun was selected to be the second core value dimension, a co-occurrence matrix that included the associated values associating with fun was constructed. A total of 59 co-occurrence rows composed the matrix. Just as for the network analysis of convenience, node-level centralities relating to fun were extracted (Table 3). As a result, nodes such as type of control, gesture shape, easy, various, convenience, good feeling, controllability, and age turned out to be important in terms of the degree centrality. Nodes such as good feeling and controllability turned out to be more important in the fun analysis than the overall network analysis. The semantic network belonging to fun is shown in Figure 5, where nodes with degree centralities of at least seven are shown and eigenvector centralities are indicated by node size.