# Consumer Perceptions and Attitudes Towards Ultra-Processed Foods
## 5 Data Analysis
### SEM Model of Customer Attitudes and Purchase Behaviour Towards UPFs
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Cronbach’sAlpha** & **DG rho** & **CR** & **AVE** & **VIF** \\ \hline Health consciences & 0.770 \({}^{*}\) & 0.799 \({}^{*}\) & 0.864 \({}^{*}\) & 0.680 \({}^{*}\) & 1.781 \({}^{*}\) \\ Knowledge about UFPs & 0.700 \({}^{*}\) & 0.804 \({}^{*}\) & 0.864 \({}^{*}\) & 0.761 \({}^{*}\) & 1.484 \({}^{*}\) \\ Subjective norms & 0.681 \({}^{*}\) & 0.817 \({}^{*}\) & 0.855 \({}^{*}\) & 0.747 \({}^{*}\) & 1.356 \({}^{*}\) \\ Environmental concerns & 0.731 \({}^{*}\) & 0.751 \({}^{*}\) & 0.847 \({}^{*}\) & 0.649 \({}^{*}\) & 1.484 \({}^{*}\) \\ Attitude towards UFPs & 0.828 \({}^{*}\) & 0.831 \({}^{*}\) & 0.897 \({}^{*}\) & 0.744 \({}^{*}\) & 1.000 \({}^{*}\) \\ Willingness to purchase & 0.680 \({}^{*}\) & 0.708 \({}^{*}\) & 0.827 \({}^{*}\) & 0.620 \({}^{*}\) & 1.000 \({}^{*}\) \\ Actual buying behaviour & 1.000 \({}^{*}\) & 1.000 \({}^{*}\) & 1.000 \({}^{*}\) & 1.000 \({}^{*}\) \\ \hline Symbol \({}^{***}\) means Cronbach’s alpha \(>\) 0.6, DG rho–Dillion-Goldstein’s Who \(>\) 0.7, CR—composite reliability \(>\) 0.6, AVE—average variance extracted \(>\) 0.5, and VIF—variance inflation factors \(<\) 5. In the presented table, most of the constructs demonstrate acceptable to high internal consistency, emphasising the reliability of the measurement scales. However, subjective norms (\(\alpha\) = 0.681) and willingness to purchase (\(\alpha\) = 0.680) show relatively lower reliability, suggesting that minor improvements to these measurement items may be beneficial. Nonetheless, the remainder of the constructs meet the recommended criteria, validation the overall reliability of the model. There are two main approaches to assessing construct reliability (i.e., repeatability): Dillon-Goldstein's rho (also known as rho_A in SmartPLS) and composite reliability (CR). For robust reliability, both DG rho and CR should be ideally above 0.7 [50]. In our study, the DG rho values ranged from 0.708 to 0.831, while the CR values spanned from 0.827 to 0.897 (see Table 5), indicating that the constructs exhibit acceptable reliability. Construct validity was evaluated through assessments of both convergent and discriminant validity. Convergent validity, which measures the consistency among different indicators of the same concept, was examined using the average variance extracted (AVE); a value of 0.5 or higher is generally considered satisfactory [51]. All constructs surpassed this threshold (Table 5), confirming good convergent validity. Discriminant validity, or the degree to which constructs are distinct from one another, was assessed using the Cornell-Larcker criterion, which requires that the square root of the AVE for each construct exceeds its correlations with other constructs. As detailed in Table 6, this condition was met for all constructs, providing strong evidence of discriminant validity. The cross-loadings analysis examines whether each item is more strongly associated with its intended construct than with any other in the model. In this study, the findings (see Table 7) show that all items load more highly on their designated constructs (italicised) than on alternative constructs. Therefore, the examination of cross-loadings supports the confirmation of discriminant validity. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Factor** & **Actual Buying Behaviour** & **Attitude Towards UPFs** & **Environmental Concerns** & **Health Consciousness** & **Subjective Norms** & **Knowledge About UFFs** & **Willingness to** **Purchase** \\ \hline Actual buying & 1 & & & & & & \\ behaviour & 0.509 & _0.863_ & & & & & \\ Environmental & 0.540 & 0.662 & _0.806_ & & & & \\ Health & 0.522 & 0.490 & 0.509 & _0.825_ & & & \\ consciousness & & & & & & \\ Subjective & 0.512 & 0.427 & 0.429 & 0.456 & _0.864_ & & \\ norms & 0.372 & 0.479 & 0.409 & 0.549 & 0.323 & _0.872_ & \\ Knowledge about UFFs & 0.632 & 0.529 & 0.507 & 0.477 & 0.454 & 0.281 & _0.788_ \\ \hline \hline \end{tabular} Note: italics represent the square root of AVE.  
\end{table}