## 3 Data Analysis and Result Interpretation
### Extracted Factors through Factor Rotation Using Q-Likert Methodology  
Q-Factor analysis, particularly the principal component analysis extraction method [50; 51] was used for both ICT groups of York and HM; analysis was run to calculate latent variables. As a result, scree plots were obtained based on Eigenvalues (<1 = factor ignored, \(\geq\)1 = factor retained) (Figure 4). Communalities for each expert set were calculated to understand the explained and unexplained information for each extracted factor. Ranging between 0 and 1, the values closer to 1 showed that extracted factors have significant information in explaining all the data Table A1. For York ICT-1, expert set 4 extracted the lowest (0.441) whilst set 8 extracted the highest (0.691) information share, and for ICT-2, expert set 1 extracted the lowest (0.145) whilst set 8 extracted the highest (0.983) information share. For HM ICT-1, expert set 8 extracted the lowest (0.170) whilst set 5 extracted the highest (0.912) information share, and for ICT-2, expert set 9 extracted the lowest (0.242) whilst set 10 extracted the highest (0.746) information share. The next step was factor rotation, the purpose of which was to analyze existing relationships but from different angles (perspectives) to unveil further details and make details less ambiguous and more explainable. There are two types of rotations that can be performed, i.e., Orthogonal and Oblique. The former rotates the factors by keeping them independent of each other, whilst the latter allows factors to co-relate with each other. Oblique rotation is very complicated compared to orthogonal rotation because correlations are permitted to happen in oblique rotation. Another compelling aspect of preferring oblique over orthogonal rotation was that the variables in this analysis are humans (experts) and the nature of their professional commitments might correlate with each other rather than being totally independent. This fact should not be ignored and was taken into account while analyzing data. Factors were rotated by using the complex method of Oblique rotation to support oblique correlations. Further to Oblique rotation, the method of rotation used was Direct Oblimin with default Delta (\(\blacktriangle\)) value = 0 so as not to influence the actual correlation that exists among the factors. \begin{table}
\begin{tabular}{c c c c c} \hline
**Case-Study ICTs** & **No. of Statements** & **(KMO)** & **[PERSON]’s Sig.** & **Sampling Adequacy** \\ \hline
**HM (ICT-1)** & 71 & 0.816 & 0.000 & Great \\
**HM (ICT-2)** & 45 & 0.679 & 0.000 & Mediocre \\
**York (ICT-1)** & 71 & 0.760 & 0.000 & Good \\
**York (ICT-2)** & 45 & 0.823 & 0.000 & Great \\ \hline \end{tabular}
\end{table} 
Table 3: Kaiser–Meyer–Olkin measure and Bartlett’s test for sampling adequacy validity. Pattern and structure matrices were calculated through factor rotation. The pattern matrix (PM) presented factor loading whilst the structure matrix (SM) presented the relationship between the obtained factors. The PM indicated the loading contributed by individual variables on each factor. Each set of Q-statements (array) and the related loadings were further interpreted with respect to the calculated and standardized scores supported with additional information taken from experts as notes. Q-Likert sorts resulted in three different types, i.e., significant, bipolar, and confound during PMs' development. Each type of Q-Likert sort was dealt with as per its nature. Table 14 shows the number of retained factors and the contribution of information shared by each expert set. For York ICTs-1 and 2, two factors were identified, and for HM ICT-1, two, whilst for ICT-2, three factors were identified. For York ICT-1, factor one was heavily loaded by set eight and two by set one. For ICT-2, factor one was heavily loaded by set five and factor two by set eight. For HM ICT-1, factor one was heavily loaded by set six and factor two by set five. For ICT-2, factor one was heavily loaded by set eight, factor two by set ten, and factor three by set two.