# Consumer Perceptions and Attitudes Towards Ultra-Processed Foods
## 3 Related Work
### Comparison of Existing Models of User Attitudes Towards Industrially Formulated Foods  
The studies presented in the preceding subsection are grounded in theoretical frameworks such as the health belief model, the theory of planned behaviour, and the knowledge-attitude-practice (KAP) model, which explore the psychological, social, and behavioural determinants of food consumption. Key factors in these models include health consciousness [33], knowledge of UPFs [37; 38; 39], social norms [31; 32], and sustainability concerns [33], all of which influence consumer attitudes, willingness to purchase, and actual buying behaviour. The TPB posits that an individual's intention to perform a behaviour is influenced by their attitudes towards the behaviour, subjective norms, and perceived behavioural control. This framework has been applied to predict healthy eating behaviours, demonstrating that attitudes and intentions are mediated by goals and needs [31; 32; 36; 40]. The HBM examines how personal beliefs about health conditions influence health-related behaviours. It suggests that individuals' perceptions of susceptibility to illness, the severity of health conditions, benefits of preventive actions, and barriers to healthcare access determine their engagement in health-promoting behaviours [38]. The KAP model assesses the relationship between knowledge, attitudes, and practices within specific contexts. It has been utilised to understand how educational interventions can modify dietary behaviours by enhancing knowledge and shaping positive attitudes toward healthy eating [32; 37; 38]. Collectively, these theoretical models provide comprehensive insights into the multifaceted factors influencing dietary decisions, thereby informing the development of effective interventions to promote healthier food choices. Most of the studies investigating consumer behaviour towards food products have utilised SEM [31; 32; 35; 36; 38; 39; 40] and confirmatory factor analysis (CFA) [33; 34] due to their suitability for complex models with multiple latent variables and smaller sample sizes. In addition, some models have been developed using alternative approaches, such as ML algorithms like classification trees, for analysis to uncover hidden patterns in consumer data [33]. The key characteristics of the models addressing the factors influencing consumer behaviour towards UPFs are summarised in Table 1, providing an overview of the methodologies, theoretical foundations, and variables explored in this research domain. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Study Design** & **Evaluation Factors** & **Statistically Significant Factors** & **Model Quality** \\  & & **(Number)** & **(s)** \\ \hline \multirow{6}{*}{[PERSON] et al., 2018[31]} & \multirow{3}{*}{SEM} & \multicolumn{3}{c}{Value for money, taste, naturalness, healthiness, cooking skills, time sensitive, social influence, market availability (9) \(\rightarrow\) consumption} & Naturalness, cooking skills, time pressure, social influence, market availability (5) & R\({}^{2}\): 0.74; 0.63 \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of models on UPFs perception: consumer attitudes, purchase intentions, and actual buying behaviour. The distribution of constructs in the above-mentioned models is as follows: purchase intention--5/10; attitude (including satisfaction)--5/10; knowledge (including cognitive attitude)--4/10; emotional value--3/10; consumption--3/10; food quality (including taste)--3/10; price (including value for money, income, and monetary resources)--3/10; convenience--3/10; functional value--2/10; time (including time pressure)--2/10; social influence (including subjective norms)--2/10; etc. The effectiveness of the models proposed in the literature, as measured using the coefficient of determination (R\({}^{2}\)), ranges from 50.0% [35] to 85.0% [32].