# Consumer Perceptions and Attitudes Towards Ultra-Processed Foods
## 5 Data Analysis
### SEM Model of Customer Attitudes and Purchase Behaviour Towards UPFs  
Based on a review of prior studies (see Section 3), there is a lack of consensus regarding the definition of inputs and outputs for evaluating consumer perceptions and attitudes towards industrially formulated food products. To address this issue, we applied the PLS-SEM method using SmartPLS (version 3.2.9) [48]. Additionally, a standard five-step procedure was followed for constructing the PLS-SEM model. Step 1. Formulate hypotheses. Seven research hypotheses were formulated at the end of Section 3, focusing on the relationships between the input and output variables. Step 2. Identify indicators for latent variables. Six constructs were measured by 19 indicator variables from the survey questionnaire. The measurement model includes 10 input indicators: health consciousness (HC): HC1, HC2, and HC3; knowledge about UPFs (UPFK): UPFK1 and UPFK2; subjective norms (SNs): SN1 and SN2; and environmental concerns (ECs): EC1, EC2, and EC3. It also comprised nine output indicators: attitude towards UPFs (ATT): ATT1, ATT2, and ATT3; willingness to purchase (WP): WP1, WP2, and WP3; and actual buying behaviour (ABB): ABB1, ABB2, and ABB3. These relationships are illustrated in Figure 3.  
Step 3. Conduct numerical modelling. We ran the PLS algorithm to estimate model parameters and evaluate the measurement and structural models. Step 4. Assess the model's suitability. If the model fits the data, proceed to Step 5. Otherwise, return to Step 3 to refine it. The initial assessment of outer loadings showed that most indicators fit well, except ABB1 and ABB2, which fell below the acceptable threshold. Consequently, both wereremoved, bringing all values within acceptable limits and allowing us to proceed with model reliability and validity verification (Step 4). In the first phase of the validity assessment, the measurement model was evaluated first, followed by the structural model. The former ensures construct validity and reliability by assessing construct reliability, indicator reliability, convergent validity, and discriminant validity, while the latter verifies the significance of the proposed relationships. Factor loadings represent how strongly each item correlates with its designated principal component. Higher absolute values indicate a stronger relationship between an item and its underlying factor. In our study, all items have loadings above the recommended threshold of 0.5 [49; 50]. The resulting measurement model and corresponding factor loadings are presented in Table 4.  
To assess multicollinearity among indicators, the study employed the variance inflation factor (VIF). A VIF value below five suggests acceptable levels of multicollinearity. Table 5 presents the VIF values for each indicator, all of which are within the acceptable range [51]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indicator** & **Factor Loading** & **Indicator** & **Factor Loading** & **Indicator** & **Factor Loading** \\
**Variable** & **Factor Loading** & **Variable** & **Factor Loading** & **Variable** & **Factor Loading** \\ \hline HC1 & 0.834 & SN2 & 0.933 & ATT3 & 0.893 \\ HC2 & 0.899 & EC1 & 0.864 & WP1 & 0.612 \\ HC3 & 0.780 & EC2 & 0.759 & WP2 & 0.851 \\ UPF1 & 0.811 & EC3 & 0.790 & WP3 & 0.873 \\ UPF2 & 0.930 & ATT1 & 0.852 & ABB3 & 1.000 \\ SN1 & 0.790 & ATT2 & 0.842 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Factor loadings for indicators. Figure 3: Measurement model with seven latent variables and 19 indicator variables.