2.1. Ethical Approval and Clearance

This study was approved by the Research Ethics Committee at Zayed University, UAE (ZU22_088_F). All study participants provided informed consent prior to beginning the online questionnaire.

2.2. Study Design

A quantitative cross-sectional study was conducted in the UAE between January and May 2023 among students, predominantly female, aged 18 and above, from Zayed University in Dubai and Abu Dhabi campuses. A structured, self-designed online questionnaire in Arabic and English through Google Forms was distributed through WhatsApp and emails.

Zayed Unversity is a govermental university in the UAE, predominantly serving Emirati female students. The university’s population aligns with national higher education trends, where women constitute a significant proportion of enrolled students due to active governmental encouragement of female education.

2.3. Sampling

A total of 1153 students were enrolled using snowball sampling. This method was chosen due to the difficulty of obtaining a comprehensive list of university students willing to participate in the study. Snowball sampling allowed us to efficiently reach a broad and diverse student population while maintaining a voluntary participation approach. However, we acknowledge that this method may introduce selection bias, as students who are more engaged in health-related discussions might be overrepresented. To mitigate this, we encouraged referrals from various academic disciplines and year groups to improve the diversity of the sample. Participants were provided information about the study and its purpose before answering the questionnaire and signed the consent form before participation; only approved participants were eligible to participate. The inclusion criteria were any student studying in Zayed University aged 18 and above. Students aged below 18 and those who did not give adequate answers were excluded. The participants had the right to withdraw from the study at any stage.

2.4. Data Collection

The data was collected using an online survey following a previously validated questionnaire while also increasing and modifying some questions and sections to ensure that the study’s goal was reached [22, 23]. Through the questionnaire, we measured how much Zayed University students know about food labels and whether they use them personally. Additionally, it assessed participants’ concerns about the expiry date and whether the product’s price influences their reading of food labels. The survey included 27 questions. Considering the students’ possible preference for one language over the other, the survey was written in English and then translated into Arabic. To ensure that the questions were clear, understandable, and error-free, the researcher reviewed them, and a pilot study of 20 participants from the College of Natural and Health Sciences was conducted to test the questionnaire. The pilot study was not included in the final collection. The questions were divided into three sections. The first section collected demographic information; it consisted of nine questions about age, gender, nationality, the emirate they live in, residential area, marital status, employment status, college, and year of study. The second section was about medical history and chronic diseases; it included only one question. The last section was about knowledge of food labeling, attitudes, and practices. It included 17 questions.

2.5. Statistical Analysis

The collected data was coded, entered, and analyzed using the Statistical Package for the Social Sciences (SPSS) version 26 (IBM, Armonk, New York, United States). Statistical tests with p values < 0.05 were considered statistically significant. Descriptive statistics were computed to summarize the questionnaire items, providing an overview of participants’ knowledge and attitudes. Chi-square tests were employed to determine the statistical significance of the percentage differences observed between variables. Pearson correlation was used to examine the relationships between different variables, while binary logistic regression was applied to determine significant predictors of food label use.

Binary logistic regression was performed to determine predictors of food label usage. The dependent variable was whether students reported checking food labels (yes/no). Independent variables included demographic factors (age, sex, marital status, and employment status), shopping behaviors, and attitudes toward food labels. The model’s assumptions were assessed for validity, and odds ratios (OR) with confidence intervals (CIs) were reported to measure the strength of associations.

3.1. Descriptive Statistics

The demographic analysis revealed a predominantly female participant group. Table 1 shows the study participants’ demographic characteristics, consisting mainly of females (93.4%) with a mean age of 21.7 years (SD = 3.9). The majority fell within the 18–20 (44.0%) and 21–24 (42.2%) age categories, while a smaller proportion (13.8%) were aged 24 and above. Most participants were UAE nationals (94.8%), primarily residing in Dubai (59.6%), Abu Dhabi (15.8%), and Sharjah (13.6%). Urban areas were the predominant residential setting (81.7%) compared to rural areas (18.3%). Singles formed the majority in terms of participants (82.4%), followed by married (15.7%), divorced (1.3%), widowed (0.3%), and separated (0.3%) individuals. Most participants (65.8%) were unemployed, while 17.3% were full-time employed, and 8.1% were part-time employed or self-employed (8.8%). The natural and health sciences college had the highest representation (29.6%), followed by business (20.3%) and humanities and social sciences (12.3%). Fourth-year students comprised the largest group (33.4%), followed by third-year students (24.8%), and then second-year students (18.8%). A minority reported having chronic disease (17.3%), while the majority did not (82.7%).

The distribution of participants’ knowledge about food labeling across various characteristics was examined using chi-square tests (Table 2). Significant associations were found between age groups (χ² = 5.27, p = 0.07) and knowledge, with the highest proportion of knowledge observed in the 18–20 age group (88.4%) followed by the 21–24 age group (83.6%). This finding suggests that younger students may have higher exposure to nutrition-related education or awareness campaigns. However, the lack of a strong significance (p = 0.07) indicates that other factors might be influencing knowledge levels beyond just age.

Marital status also showed a significant association (χ² = 7.67, p = 0.10), with married participants having more knowledge (80.7%). This could be attributed to the fact that married individuals may take greater responsibility for grocery shopping and meal preparation, making them more inclined to read and understand food labels. However, as the p value is above the significance threshold, this association should be interpreted with caution.

There were no significant associations between gender (χ² = 1.12, p = 0.29), frequency of going shopping (χ² = 2.65, p = 0.45), or emirates of residence (χ² = 6.83, p = 0.34) and knowledge about food labeling. However, a significant association was found between educational colleges (χ² = 14.17, p = 0.05), with the natural and health sciences college having the highest proportion of knowledge (90.9%).

Most participants indicated knowledge about the importance of food labeling, with 89.9% responding affirmatively and acknowledging its significance. A smaller proportion of respondents (10.1%) showed a lack of knowledge about the importance of food labeling. Regarding familiarity with food labels, 85.7% of participants reported knowing what a food label is, while 14.3% indicated a lack of knowledge in this area (Figure 1).

Table 3 summarizes the participants’ distribution based on their practices related to food labeling. Over half (55.6%) of participants reported checking all the food labels for items they buy. Similarly, 58.6% of participants indicated that they replace food items based on the importance and value of food labeling. Regarding cost-based replacement, 61.8% (n = 712) of participants reported replacing food items based on cost. When asked about buying food items with no food labels, 41.9% of participants reported that they do so, while the majority (58.1%) reported not purchasing such items. The information participants looked for on food labels varied, with the highest percentages for calories (67.0%) and nutritional value (49.7%).

Regarding checking expiry dates, 85.8% of participants reported checking the expiry dates of every food item they buy, while 14.2% indicated not practicing this behavior. Additionally, only a small proportion (16.5%) reported buying expired items if available at low cost or for free, while the majority (83.5%) declared not engaging in such practice. Furthermore, most participants (58.1%) reported that low prices attract them to buy food items. In terms of preferring branded food items, the majority (74.0%) of the participants expressed a preference for branded items.

Similar to the analysis of knowledge about food labels, we compared attitudes toward food labeling among participants with and without chronic diseases. Table 4 shows a significant association between attitudes toward the importance of food labeling and the presence of chronic diseases among the study participants (χ² = 4.49, p = 0.034). This result suggests that individuals with chronic diseases are more likely to recognize the importance of food labeling compared to those without chronic diseases.

The results also showed a statistically significant relationship between the year of study and knowledge about food labels (χ² = 12.245, p = 0.016). The chi-square test indicates that fourth-year students had the highest percentage of knowledge about food labels (87.8%), while first-year students had the lowest (75.8%). This suggests that knowledge about food labels tends to increase as students advance in their academic years.

3.2. Pearson Correlation

We computed the Pearson correlation between knowledge, attitude toward food labels, and behavior patterns concerning food labeling among different demographics. The results are shown in Tables 5 and 6. Table 5 shows a moderately positive correlation between knowledge and attitude (0.265, p < 0.01), suggesting that as knowledge about food labeling increases, attitudes toward the importance of labeling also become more positive. This finding indicates that awareness often drives behavioral change. However, the moderate strength of the correlation suggests that knowledge alone is not sufficient to guarantee a strong positive attitude—external factors such as trust in food labels, accessibility of labeled products, or social influences might also play a role.

Furthermore, attitude toward food labeling shows a positive correlation with the practice of checking the expiration date (0.184), indicating that those with a more favorable view of food labeling are more diligent in checking expiration dates on food products.

Table 6 shows correlations between different perceptions and beliefs about food labeling. We observed a negative correlation between attitudes toward food labeling and perceptions toward food labels, which indicates that individuals with a positive attitude toward food labeling tend to be more skeptical about the source’s trustworthiness and the various functionalities that labels are presumed to serve. Conversely, trustworthy source has strong positive correlations with the other perceptions about food label variables such as help health (0.406, p < 0.01), time reading (0.387, p < 0.01), regulate calories (0.335, p < 0.01), and easy label (0.396, p < 0.01), which indicates that those who find food labels to be a reliable source of information are also more likely to believe that labels can help with health management, take the time to read them, and find them effective for calorie regulation and easy to use.

3.3. Regression Analysis

Logistic regression analysis was conducted to identify key predictors of food label usage (dependent variable: checking food labels—yes/no). Independent variables included demographic factors (age, gender, marital status, and employment status) and shopping behaviors. This analysis helps determine which factors influence whether students actively engage with food labels (Table 7).

The model’s intercept was statistically significant (p = 0.002), suggesting that the model does have a predictive value, although it does not come from these predictors. However, none of these demographic variables significantly predicted knowledge about food labeling, which is surprising given that prior literature often finds age and education level to be strong predictors. This could be due to the homogeneity of the sample, as most participants were university students of similar age and background.

Nonetheless, when considering attitudes toward the importance of food labeling, marital status and employment status emerged as significant predictors, with p values of 0.053 and < 0.001, respectively. Although the p values for marital status are slightly above the standard threshold of 0.05, it is close enough to warrant consideration.

The odds ratio for marital status (1.877) suggests that married individuals are 1.877 times more likely to perceive food labeling as necessary. At the same time, those with different employment statuses have a decreased likelihood of perceiving food labeling as necessary, with an odds ratio of 0.443. Regarding “checking and reading” food label variables, employment status was a significant predictor (p = 0.019), but age, gender, and marital status were not significant predictors. The odds ratio of employment status (1.370) indicates that the likelihood of checking and reading food labels varies based on employment status, with those being employed full-time or part-time being 1.370 times more likely to check and read food labels compared to other employment statuses.

This finding highlights the potential impact of life responsibilities on attitudes toward food labeling. Married individuals and employed participants may have more exposure to practical decision-making related to food purchases, reinforcing the relevance of food labels in their daily lives. The strong significance of employment status (p < 0.001) suggests that financial independence may also play a role in label-checking behavior, possibly because employed individuals have more control over their food choices.

4.1. Attitudes Toward Food Labels

Despite high awareness levels, actual food label utilization remains inconsistent. While 55.6% of participants reported checking labels regularly, a substantial proportion (44.4%) did not, similar to findings from studies in the United States and Europe, where many consumers fail to translate awareness into regular use [10, 28–31]. The most checked information on food labels included calories (67.0%) and nutritional value (49.7%), suggesting that students are concerned about weight management and health. However, this contrasts with studies from the UAE, Sudan, and Saudi Arabia, where consumers prioritized expiration dates over nutritional details [12, 22, 32]. This highlights a shift in awareness, where younger populations are more engaged with calorie monitoring than previous generations.

Interestingly, 41.8% of students considered food labels a trustworthy source, while 41.0% expressed some skepticism. This contradicts prior studies that found a high level of distrust in food labeling, particularly in regions with weaker regulatory enforcement [20]. The higher trust levels observed in our study could indicate growing confidence in food regulations within the UAE, possibly influenced by government-led initiatives promoting transparency in food labeling [33].

4.2. Impact of Chronic Disease Status on Label Use

A significant association was found between attitudes toward food labels and chronic disease status (χ² = 4.49, p = 0.034). Students with chronic illnesses were more likely to value food labels, aligning with studies in South Korea and the United States, where patients managing diabetes, hypertension, or obesity were more likely to use nutritional labels [34, 35]. However, this contrasts with findings from studies in India and Turkey, where chronic disease patients were often unaware of food labeling regulations [33, 35]. Our study suggests that health education programs targeting chronic disease patients in the UAE may be more effective than in other regions.

4.3. Behavioral Trends and Influencing Factors

The price of food remained a dominant factor in purchasing decisions, with 61.8% of students replacing food items based on cost. This supports previous findings that financial constraints significantly influence food choices, particularly among college students and low-income populations [22, 36–38]. Additionally, 58.1% of students admitted being influenced by low prices, reinforcing studies from Malaysia and Australia, which found that financial barriers often override health considerations in food selection [38, 39]. This suggests that awareness alone is insufficient for promoting healthier food choices and calls for policy interventions that make healthier food options more affordable.

4.4. Demographic Predictors of Food Label Use

Regression analysis showed that marital status and employment status were the strongest predictors of attitudes toward food labeling (p = 0.053 and p < 0.001, respectively). Married students were nearly twice as likely (OR = 1.877) to perceive food labeling as important, likely due to their responsibility in household food purchasing. This is consistent with findings from studies in Canada and the United Kingdom, where married consumers demonstrated greater label engagement [40, 41].

Similarly, employed students were 1.370 times more likely to check food labels, which aligns with research from some countries like Canada and Brazil, where higher income groups showed greater label use [39, 42, 43]. This suggests that financial independence plays a key role in food label engagement. However, unlike previous studies that reported age and gender as strong predictors of food label use [41, 44], these factors were not significant in our study. This may be due to the homogeneous nature of the sample, where most participants were young, female university students. These results suggest that targeted education campaigns should consider life-stage factors to enhance food label literacy among students.

4.5. Policy and Education Implications

Our findings suggest that nutrition education programs should be expanded beyond awareness campaigns to address behavioral barriers, particularly cost-related obstacles to healthy eating. Public health initiatives should target students with chronic diseases, given their higher likelihood of benefiting from food label use. Additionally, policymakers could explore incentives for healthier food choices, such as subsidized pricing for nutritious products and mandatory clearer front-of-pack labeling.

These findings highlight that high awareness of food labels does not always translate into consistent use, with price and convenience still dominating food selection. However, the strong associations between chronic disease status, employment, and label use suggest key groups that would benefit from targeted nutrition education. Moving forward, public health strategies should focus on making healthier food choices more accessible and affordable while reinforcing trust in food labeling as a reliable source of information.

4.6. Limitations

This study has some limitations that should be acknowledged. First, the sample was limited to Zayed University students, making it difficult to generalize findings to other universities or broader populations. Future research should include a more diverse sample across different institutions and demographics to provide a more representative understanding of food labeling knowledge and behaviors.

Second, this study did not examine how misinformation, skepticism, or negative perceptions about food labels affect consumer behavior. Prior research suggests that some individuals distrust food labels due to perceived inaccuracy or industry bias, which can significantly impact their usage. Addressing this gap could provide deeper insights into barriers to effective label use.

Third, the study relied on self-reported data collected through an online survey, which may introduce response bias or misinterpretation of questions. Without direct researcher clarification, participants may have misunderstood certain terms or provided socially desirable responses rather than reflecting their true practices. Future studies should consider mixed-method approaches, such as in-depth interviews or observational studies, to validate self-reported behaviors. Moreover, the study did not explore the long-term impact of food label literacy on actual dietary habits. While many students reported checking food labels, it is unclear whether this translates into healthier eating patterns over time. Longitudinal studies could assess whether food label awareness leads to sustained improvements in diet and health outcomes.