INTRODUCTION
Animal welfare includes animals’ physical, psychological, and behavioral health, shaped by environmental and husbandry conditions, including pre-slaughter stress (Ndou et al., 2011; WOAH, 2019), while ethical food choices reflect consumers’ morally guided decisions related to animal welfare, environmental sustainability, and human health (Willett et al., 2019). These concepts are closely linked within the farm-to-fork approach, which emphasizes sustainability, transparency, and food safety across the entire food supply chain (European Commission, 2015). Animal welfare in livestock production refers to protecting animals from hunger, thirst, discomfort, pain, disease, fear, and distress, while allowing natural behaviours (International Dairy Federation, 2008; Broom and Fraser, 2015) and poor welfare causes stress that can impair health, growth, reproduction, and product quality (Grandin, 1993; Hewson, 2003; Arsoy, 2020).
Animal welfare is central to the ONE Welfare approach, linking animal, human, and environmental health with food safety and sustainability (Arsoy, 2023), and it’s increasingly recognized in food policies shaped by scientific, ethical, economic, cultural, religious, trade, and sustainability considerations (Mench, 2008; Alonso et al., 2020; Arsoy, 2022). Rising global demand for animal-based protein has intensified livestock production, raising concerns over animal welfare, environmental impacts, and resource use, while accounting for around 80% of agricultural land use (Ritchie and Roser, 2019; Arsoy and Uygun, 2023). Industrial livestock systems impose hidden economic costs and contribute significantly to pollution, biodiversity loss, and climate change, accounting for around 14.5% of global emissions (Gerber et al., 2013; Ritchie and Roser, 2019). The methods applied in animal production are a major threat to human health, especially by using antibiotics and growth hormones in terms of ONE Health framework (Arsoy, 2022; Arsoy, 2023). This issue is also reflected in global approaches such as One Welfare (Pinilloset al., 2016) and FAO’s sustainable food systems framework (FAO, 2018).
The emergence of antibiotic-resistant bacteria is a major global health challenge (WHO, 2017). Pathogens such as Salmonella and E. coli can cause severe foodborne diseases through contaminated animal products (Gerber et al., 2013), while the widespread use of antibiotics and growth promoters in livestock production further contributes to antimicrobial resistance (WHO, 2015; Baptiste and Kyvsgaard, 2017). These concerns have increased the importance of animal welfare, prompting the EU to implement stricter regulations to improve farming conditions (Bennett and Blaney, 2003). Animal welfare is closely linked to food safety and security, as production practices directly influence public health outcomes (Blokhuis et al., 2003). Accordingly, regulatory frameworks, including restrictions introduced by the European Commission and monitoring systems established under the European Food Safety Authority (EFSA), aim to safeguard animal and human health within the food system (European Commission, 2015; Bozzo et al., 2021).
Previous studies have highlighted that animal welfare can influence food-related attitudes and preferences of consumers. Ethical food choices are often explained via behavioral frameworks such as the Theory of Planned Behavior (TPB) (Ajzen, 1991) and the Value-Belief-Norm (VBN) theory (Stern et al., 1999), which highlight the role of attitudes, norms, and values. These approaches are particularly relevant for university students, who represent both current consumers and future opinion leaders.
Recent studies have extended and integrated these frameworks to better explain sustainable and ethical food choices (Chang et al., 2026; Chi et al., 2026; Wang and Chang, 2026). For instance, Chang and Chen (2022) found that TPB factors significantly influence intentions to buy animal welfare-friendly milk.Pasquariello et al. (2025) show that both TPB constructs and moral norms derived from VBN significantly influence consumers’ intentions toward plant-based food consumption. Similarly, a recent study confirms that knowledge, environmental concern, and social norms play a crucial role in shaping sustainable dietary behaviors, particularly among younger consumers (Civero et al., 2025). Moreover, recent VBN-based study shows that altruistic and biospheric values influence animal welfare beliefs and drive plant-based repurchase intentions (Wang and Chang, 2026). A similar evidence from Northern Cyprus shows that university students may express positive attitudes toward sustainable product attributes, while their willingness-to-pay remains highly sensitive to price and product characteristics (Çelik et al., 2026).
Consumers are increasingly aware of credence attributes, such as food safety and animal welfare (Bernués et al. 2003; Cheftel, 2005). When purchasing products, consumers often prioritize qualities that support animal welfare and environmentally responsible production practices (Bernués et al., 2003; De Passillé and Rushen, 2005).
Socio-demographic factors such as gender, income, and education shape consumer awareness (Lassen et al., 2006; Grgić et al., 2025). Moreover, recent research shows that, U.S. consumers remain focused on price and safety (Barahona-Domínguez et al., 2025), Bosnian youth exhibit high awareness and willingness to pay (Grgić et al., 2025), and Swiss consumers place animal welfare above GHG reductions (Richter et al., 2025).
To the best of our knowledge, no prior study has examined animal welfare perceptions and ethical food choices among university students’ in Northern Cyprus. This study explores the gap by analyzing students’ awareness, preferences, and willingness to pay for welfare-friendly products, with a focus on socio-demographic factors, labelling, and attitudes toward alternative proteins.
MATERIALS AND METHODS
The Study Area and Sample Size: A quantitative research design was employed to collect data from a sample of students enrolled in health-related faculties at a university, located in Nicosia, Northern Cyprus from January to March, 2023. The participants were drawn from the Faculties of Medicine, Veterinary Medicine, Nursing, Health Sciences, Dentistry, and Pharmacy. Data were collected through face-to-face questionnaire, and respondents were given 20 minutes to complete them. Participants were selected using a convenience sampling method (Emerson, 2015), as the study targeted students enrolled in health-related faculties who are expected to have higher level of awareness regarding food safety and animal welfare issues. This method was preferred due to its practicality and accessibility, allowing efficient data collection within a defined population.
To determine the maximum sample size for the research area, the P and Q values were set at 0.50. Based on this, the sample size was calculated to be 202 participants with a 95% confidence level and a 6.9% margin of error (Churchill, 1995). The formula used is as follows:

P: Positive probability (50%)
Q: 1-P Negative probability (50%)
Zx/2: Confidence interval (95%, table value 1.96)
d: Margin of error (6.9%)
(1)
Although data were initially collected from 202 participants, the final analysis was conducted with 200 valid questionnaires after data screening.
The Questionnaire Development: The questionnaire was structured into three distinct sections. The first section collected socio-demographic data to establish a diverse and representative sample, including variables such as age, gender, field of study, and academic level. The second section evaluated participants’ awareness and knowledge regarding animal welfare and production practices, aiming to gauge their level of understanding and concern related to these issues. The third section focused on participants’ attitudes toward animal welfare, addressing key factors such as feeding, housing, breeding, and the physical and behavioral health conditions of animals. The questionnaire items were based on an extensive review of the relevant literature, ensuring both relevance and validity in addressing the research objectives (Napolitano, 2008; Clark et al., 2016; Wolf and Tonsor, 2017).
Data Analyses: The data collected in this study were analyzed using the SPSS 27.0 Software. To assess the distribution of the data, a Shapiro-Wilk test (Razali and Wah, 2011) was performed. Descriptive statistics were employed to measure consumer awareness regarding the effects of animal production techniques and welfare on food safety and security. Additionally, a normality test was conducted to evaluate the distribution of the data. The skewness and kurtosis values indicated that the data for all variables were not normally distributed. Based on the guidelines provided by George and Mallery (2021), the acceptable range for skewness and kurtosis in a normality test is between -2 and +2. Since the data were not normally distributed, non-parametric tests were applied in the inferential analysis (Field, 2024). The Kruskal-Wallis test was used to compare differences across socio-demographic groups more than two categories. When significant differences were identified, pairwise comparisons were examined using Mann-Whitney U tests with Bonferroni adjustment (Mann and Whitney, 1947; Kruskal and Wallis, 1952). Furthermore, Spearman correlation coefficients were used to assess the strength and direction of monotonic relationships between variables. This non-parametric method was chosen due to its suitability for ordinal data and non-normal distributions (Bonett and Wright, 2000).
RESULTS
Socio-demographic Characteristics of Respondents: The socio-demographic profile of the respondents in this study is summarized in Supplementary Material (Table S1). Out of 200 participants, identified as female (58.0%), as male (40.0%), and as non-binary (2.0%). The majority of participants were aged 18-25 (88.78%, n = 177), followed by 26-35 (10.75%, n = 22), while only 0.47% (n = 1) were aged 36-45. In terms of nationality, the majority of participants were from Türkiye (55.61%), Nigeria (14.43%), and North Cyprus (7.01%). The remaining participants (22.95%) were from various other countries (Jordan, Sudan, Tanzania, Iraq, Iran, Kenya, Lebanon and so on). All participants, comprising undergraduate (98.60%) and postgraduate students (1.40%), were from health-related disciplines. Regarding religion, most respondents identified as Muslim (73.36%), followed by Christians (16.36%), while the remaining (11.21%) reported various other affiliations or no religious belief. The participants reported a monthly income in the majority group is below $100 (31.31%).
Young Consumers' Awareness and Preferences towards Animal-Based Products and Artificial Meat: Most respondents (87.85%) regularly consume animal-based products. As shown in Table 1, white meat (99.07%) and eggs (99.53%) are the most consumed, while pork (35.51%) is the least consumed, consistent with the predominantly Muslim sample (73.36%, p<0.05). Goat, cattle, and sheep are the most commonly consumed meats, whereas internal organs show relatively lower consumption (60.28%, p<0.05). The main reasons for not consuming animal-based products are individual preferences (35.51%) and religious considerations (22.43%). Awareness of poor animal welfare conditions is notably low (1.87%), although 19.6% perceive meat products as unhealthy. Knowledge of artificial meat remains limited: 52.34% report no information and 41.12% only minimal knowledge. When priced equally, 57.00% would not choose artificial meat, while 21.50% would sometimes and 21.50% would choose it. A majority (53.73%) perceive it as completely different from conventional meat. Vegetarianism is low (3.74%), and 91.12% believe plant-based products differ from animal-based ones. Willingness to consume vegetarian meat at equal price is 23.83%, and only 3.74% consider it equivalent in terms of health and development. Among non-consumers, key reasons include poor hygiene and health conditions (34.14%), sensory dislike (12.62%), and zoonotic disease concerns (7.48%), while environmental and related concerns are least cited (1.40%). Label awareness is high (84.58%), with strong interest in detailed product information (72.9%). Willingness to pay more for welfare-friendly products is 33.3%, with most accepting up to a 10% premium (53.74%). Additionally, 86.92% are willing to buy organic products, and 60.28% would do so if prices were equal (see in Table 1).
Table 1. Descriptive Findings on Consumer Awareness, Consumption Behavior, and Perceptions of Animal Welfare and Alternative Food Options
|
Features
|
Groups
|
%
|
|
1. Animal-based product consumption
|
Some
|
8.41
|
|
Vegetarian
|
3.74
|
|
All
|
87.85
|
|
2. Consumers' preferences for animal products*
|
Red meatabcdef
|
86.92
|
|
White meatbcdg
|
99.07
|
|
Internal organscefg
|
60.28
|
|
Processed animal productsdef
|
73.83
|
|
Eggeg
|
99.53
|
|
Milkfg
|
96.73
|
|
Allg
|
83.64
|
|
3. Consumers' choice of red meat animal species*
|
Goatacde
|
98.13
|
|
Cattlebcde
|
96.73
|
|
Sheepcde
|
93.46
|
|
Pigdabce
|
35.51
|
|
Noneed
|
76.17
|
|
4. The reasons for not consuming animal-based products.
|
Personal preferences
|
35.51
|
|
Religion factor
|
22.43
|
|
Because it is unhealthy
|
19.16
|
|
Because it is not suitable
|
17.29
|
|
Emotional factor
|
3.74
|
|
Animal welfare conditions are not good
|
1.87
|
|
5. Do you have information about Artificial meat?
|
A little
|
41.12
|
| |
No
|
52.34
|
| |
Very good information
|
6.54
|
|
6. Is Artificial meat equal to normal meat for health?
|
Absolutely different
|
53.73
|
|
Absolutely the same
|
4.21
|
|
Similar
|
4.68
|
|
There are some differences
|
37.38
|
|
7. If the same price, would you choose Artificial meat over red/white meat?
|
No
|
57.00
|
| |
Sometimes
|
21.50
|
| |
Yes
|
21.50
|
|
8. If the same price, would you choose plant-based options (soy meat/milk)?
|
No
|
49.07
|
|
Sometimes
|
27.10
|
|
Yes
|
23.83
|
|
9. Does Artificial meat match normal meat in benefits?
|
Absolutely different
|
67.76
|
|
Absolutely the same
|
3.74
|
|
Similar
|
5.14
|
|
There are some differences
|
23.36
|
|
10. Is being a vegetarian equal to eating meat?
|
Absolutely different
|
67.76
|
| |
Absolutely the same
|
3.74
|
| |
Similar
|
5.14
|
| |
There are some differences
|
23.36
|
|
*Means with different superscripts (a,b,c,d,e,f,g) within the same column are statistically different (p<0.05)
|
|
|
The Kruskal-Wallis H test showed statistically significant gender differences in all three dependent variables. Female respondents reported stronger preferences for vegetable-based alternatives, higher animal product consumption ranks, and greater willingness to pay for animal welfare-labelled products than male respondents (see in Table 2).
Table 2. Kruskal-Wallis Test Results by Gender on Animal Product Consumption, Alternative Preferences, and Willingness to Pay
|
Dependent Variable
|
H
|
df
|
p-value
|
|
If the price is same, would you prefer vegetable meat and milk instead of red or white animal meat and milk?
|
27.33
|
2
|
0.001
|
|
Do you consume animal products?
|
9.57
|
2
|
0.008
|
|
Would you pay more for a product with the animal welfare (happy animal) logo?
|
8.44
|
2
|
0.015
|
Note: Values are based on the Kruskal–Wallis H test. Only gender was included in this revised table because significant differences were observed for this variable. Post hoc pairwise comparisons indicated that the main significant differences were between female and male respondents.
Fig. 1 presents a histogram of participants’ willingness-to-pay (WTP) more for animal welfare labelled (happy animal) logo. The largest group was concentrated at the 10% premium level (n=101), followed by the 20% premium level (n=38) and the 30% premium level (n=23). The number of respondents declined at higher premium levels, indicating that most participants were willing to pay only a modest additional amount. Female participants were more represented than male participants in most WTP categories. At the highest premium level, 100%, only six respondents were represented, suggesting limited willingness to pay a very high premium for animal welfare-labelled products.

Figure 1. Willingness-to-Pay Premium for Animal Welfare Logo by Gender (n=200)
Consumer Awareness Regarding Animal Welfare and Production Techniques: The majority of participants, 46.3%, reported having knowledge about animal welfare, and 19.6% had no knowledge. Regarding animal production techniques, 36.5% indicated awareness. When asked about the healthiest or most suitable animal production method, 46.3% preferred organic production (see in Table 3).
Table 3. Consumer Awareness of Animal Welfare and Animal Production Techniques
|
Features
|
Groups
|
Distribution
|
|
n
|
%
|
|
Do you have any idea about Animal Welfare (Livestock or food animal, farm animals {Cattle, Pig, Chicken, Sheep, Goat})
|
A little bit
|
50
|
25.2
|
|
No
|
39
|
19.6
|
|
Not sure
|
18
|
8.9
|
|
Yes
|
93
|
46.3
|
|
Do you have any idea about animal production techniques (Conventional, Organic, Good animal practice, Traditional/ecologic, pasture)
|
A little bit
|
53
|
26.6
|
|
No
|
45
|
22.4
|
|
Not sure
|
29
|
14.5
|
|
Yes
|
73
|
36.5
|
|
Which animal production method is healthier/better for you to consume animal products?
|
Conventional
|
9
|
4.7
|
|
Good animal practice
|
33
|
16.4
|
|
Organic
|
93
|
46.3
|
|
Traditional
|
44
|
22.0
|
|
I don't know/Not important
|
21
|
10.6
|
Note: Values are reported as frequencies (n) and percentages (%); n=200.
The reliability of the Animal Welfare Perception Scale and its subscales was assessed, with Cronbach's Alpha coefficients, arithmetic means, and standard errors presented in the Supplementary Material (Table S2). The overall reliability of the general Animal Welfare Scale was 0.96, while the subscales, feeding, breeding, housing, animal health conditions, and animal behavior recorded Cronbach's Alpha coefficients of 0.74, 0.89, 0.91, 0.87 and 0.80, respectively (Nunnally, 1978).
Findings Regarding Relationships Between Respondents' Socio-Demographic Characteristics and Animal Welfare Perception: As presented in Supplementary Material (Table S3), socio-demographic factors partly shape respondents’ perceptions of animal welfare. Gender shows significant differences across several dimensions (p < 0.05), particularly in feeding conditions (e.g., nutrition and water access), selected housing aspects (overcrowding and hygiene), health-related factors (such as caretaker behavior, biosecurity, and veterinary access), and certain behavioral indicators (e.g., freedom of movement and bonding). Nationality also plays a significant role, with differences observed across breeding, housing, health, and behavioral conditions (p < 0.05). In comparison, religion has more limited but still notable effects, mainly related to perceptions of individual housing and separation from mothers, while income does not show a statistically significant association with animal welfare perceptions (p > 0.05). Furthermore, the findings in Supplementary Table S3 indicate that respondents exhibit relatively higher awareness of animal health-related issues, particularly water conditions, access to on-site veterinary services, vaccination, general health status, and slaughter conditions. In contrast, awareness of certain breeding practices remains comparatively low, especially for practices such as nail trimming and dehorning.
The Relationship between consumers' perceptions towards food labeling, animal welfare, and willingness-to-pay for animal-based food: The relationship between consumers’ perceptions of animal welfare and food safety and security variables—such as checking product labels (1, 2), obtaining information on gender, nationality, religion, income, breed, welfare, and rearing conditions, purchasing behavior based on welfare labels (3), and willingness to pay more for products (4) with animal welfare logos, was analyzed using Spearman’s correlation.
A few significant relationships were identified. Nationality correlated positively with seeking label information and purchasing products labeled as manufactured to animal welfare standards (r = 0.170; p <0.05 and r = 0.180; p < 0.01).
Animal selection techniques and the presence of health issues (e.g., lameness, coughing) positively correlated with label-checking behavior (r = 0.164; p <0.05 and r = 0.160; p <0.05). Conversely, animals housed alone or in barns, living conditions, as well as bonding, negatively correlated with seeking label information (see in Table 4).
Table 4. Spearman Correlation Results Between Animal Welfare Perception Dimensions and Food-Related Consumer Behavior
|
Features
|
1
|
2
|
3
|
4
|
|
Gender
|
-0.038
|
0.114
|
0.108
|
-0.068
|
|
Nationality
|
0.004
|
.170*
|
.180**
|
0.026
|
|
Religion
|
0.002
|
0.114
|
0.051
|
0.046
|
|
Income
|
0.066
|
0.058
|
0.015
|
-0.017
|
|
Nutrition Condition
|
0.019
|
0.029
|
-0.025
|
-0.063
|
|
Water Condition
|
0.073
|
0.007
|
-0.051
|
-0.019
|
|
Farming Conditions
|
0.04
|
-0.014
|
-0.065
|
-0.069
|
|
Selection Technique
|
.164*
|
-0.092
|
-0.047
|
-0.083
|
|
Artificial Insemination Technique
|
0.014
|
-0.086
|
0.021
|
-0.077
|
|
Benefit from Pasture
|
0.031
|
-0.041
|
-0.01
|
0.042
|
|
Nail Trimming
|
0.004
|
-0.132
|
-0.033
|
-0.101
|
|
Dehorning, Tailing, Debeaking
|
0.042
|
-0.022
|
-0.007
|
-0.095
|
|
Overcrowded Housing Condition
|
-0.017
|
0.007
|
-0.02
|
-0.089
|
|
Living Condition
|
-0.054
|
-.138*
|
-0.031
|
-0.057
|
|
Barn Climate Condition
|
-0.058
|
-0.047
|
-0.014
|
-0.053
|
|
Quality/Hygiene Condition
|
0.004
|
-0.037
|
-0.026
|
0.004
|
|
Milking and Udder Hygiene
|
-0.031
|
-0.066
|
0.031
|
-0.008
|
|
Housing Material/Shape
|
0.016
|
-0.018
|
0.006
|
-0.062
|
|
Number of Feeders/Drinkers
|
-0.021
|
-0.118
|
-0.042
|
-0.013
|
|
Caretaker Behavior to Animal
|
0.032
|
-0.102
|
-0.059
|
-0.041
|
|
Biosecurity Measures
|
0.002
|
-0.031
|
0.031
|
-0.043
|
|
Animal Vaccination
|
0.06
|
0.036
|
0.009
|
-0.058
|
|
On-Site Veterinarian
|
0.047
|
0.045
|
0.088
|
0.01
|
|
Animal Health Issues
|
.169*
|
0.029
|
0.006
|
-0.025
|
|
Pest Infestation Issues
|
-0.048
|
-0.043
|
0.007
|
-0.096
|
|
Transportation Condition
|
-0.027
|
-0.1
|
-0.046
|
0.033
|
|
Slaughter Condition
|
0.025
|
0.025
|
-0.063
|
-0.103
|
|
Separation from Mothers
|
-0.037
|
-.146*
|
-0.036
|
-0.095
|
|
Freedom of Movement
|
0.062
|
-0.048
|
-0.054
|
-0.102
|
|
Food and Water Access
|
0.001
|
-0.047
|
-0.026
|
-0.055
|
|
Individual Housing of Young Animals
|
0.036
|
-.142*
|
0.045
|
-0.065
|
|
Bonding of Animals
|
-0.054
|
-.142*
|
-0.025
|
-0.078
|
Note: 1-Do you look at the label of the product you buy?; 2-Would you like to have information such as the origin, region, sex, breed, condition, rearing of the animal on the label? 3-How do animal welfare labels impact your purchase decision?; 4-Would you pay more for a product with the animal welfare logo?
* Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
DISCUSSION
This study shows that although animal-based product consumption remains high among university students, animal welfare considerations have limited influence on actual food choices. This finding is consistent with recent research indicating that consumers still prioritize price, taste, and health over ethical and environmental concerns (Ammann et al., 2024; Barahona-Domínguez et al., 2025). The very low proportion of respondents avoiding products due to animal welfare concerns confirms the persistence of the attitude-behavior gap (Carrington et al., 2010; Essiz et al., 2023).
From a theoretical perspective, these results can be explained through recent extensions of the TPB and VBN framework. Studies show that knowledge, moral norms, and social influence are key drivers of sustainable consumption (Pasquariello et al., 2025; Civero et al., 2025). However, the limited role of animal welfare in consumption decisions in this study suggests that these mechanisms are not fully activated. Similarly, Wang and Chang (2026) demonstrate that altruistic and biospheric values influence animal welfare beliefs, but their behavioral impact depends on the strength of internalized norms.
Cultural and religious factor appear to play a more decisive role. The low consumption of pork and dominance of personal preferences confirm that food choices are strongly shaped by socio-cultural norms rather than ethical considerations alone, supporting previous findings (Font-i-Furnols and Guerrero, 2014; Grgić et al., 2025). The gap between interest in labelling and willingness to pay is also evident. While most respondents seek detailed product information, only a limited share is willing to pay a premium. This supports recent studies emphasizing that trust and perceived credibility are critical for label effectiveness (Lim and Page, 2022; Kuchler et al., 2023).
Resistance to alternative proteins further reinforces these findings. Most respondents are unwilling to substitute conventional meat with artificial or plant-based alternatives, which is consistent with recent literature highlighting low consumer acceptance due to perceived risks, lack of familiarity, and attachment to traditional diets (Sestino et al., 2023; Civero et al., 2025). This also aligns with Mendoza et al. (2024), who emphasize that perceived benefits, transparency, and trust in production processes are essential for improving acceptance of novel protein technologies. The high poultry and egg consumption observed here suggests that young consumers may be less aware of welfare issues tied to these products. Only 1.87% of respondents reported poor welfare as a reason to avoid certain foods, while most cited personal preferences, health, or religion. This finding aligns with Barahona-Domínguez et al. (2025), who reported low awareness of broiler welfare in the U.S., where choices were driven primarily by price and safety. In contrast, Grgić et al. (2025) found much higher awareness (79%) and willingness (91%) among Bosnian students.
A central finding is the persistence of the attitude–behavior gap: although concern for welfare is rising, it does not consistently translate into purchasing behavior. Price and cultural norms remain decisive, confirming Napolitano (2008) and Carrington et al. (2010). Food labeling plays an important role in narrowing this gap. Many respondents sought detailed labeling on production and welfare practices, consistent with Bernués et al. (2003) and Lim and Page (2022). However, skepticism toward certification undermines effectiveness, as highlighted by Kuchler et al. (2023).
Gender differences were also evident. Female consumers express greater empathy for animals and a stronger willingness-to-pay for welfare-certified products, as supported by Grgić et al. (2025). These results reinforce the importance of gender-sensitive strategies when promoting ethical and sustainable foods. Cultural and religious influences were also clear, particularly regarding pork consumption, which supports the findings of Font-i-Furnols and Guerrero (2014).
In the present study, more than half of respondents reported they would never consume artificial meat even at price parity, highlighting a strong resistance that mirrors earlier findings of limited consumer acceptance (Wilks and Phillips, 2017; Kantor and Kantor, 2021).This reluctance reflects a broader challenge in sustainable food consumption, where trust, price, and skepticism often constrain consumer acceptance (Çelik and Gül, 2023). Together, these results highlight that while young consumers increasingly value sustainability, adoption of novel proteins and certified products remains constrained. Finally, this study confirms the close link between animal welfare practices and food safety. Ethically farmed products are often perceived as healthier, tastier, and safer (Napolitano, 2008; Daley et al., 2010).
Overall, the findings suggest that while ethical and sustainability-related attitudes are present among young consumers, their influence on behavior remains constrained by cultural norms, limited knowledge, trust issues, and economic considerations.
Conclusions: This study shows that there is still a clear attitude-behavior gap in ethical food consumption and animal welfare, showing that although young health science students recognize the importance of ethical production, their purchasing decisions were still strongly influenced by price, convenience, and cultural habits. Female participants appeared to be more concerned about animal welfare and were more willing to pay for ethically produced food. Labels also played a crucial role in shaping choices, but doubts about whether these labels are truly trustworthy reduced their impact. In addition, the low acceptance of alternative proteins such as artificial meat suggests that unfamiliarity, cultural habits, and limited knowledge continue to affect consumer choices. Overall, the findings suggest that better consumer education, clearer information, and more reliable labeling systems are needed to encourage more ethical and sustainable food choices.
The study has some limitations. First, the analysis is based on a sample of university students, which may limit generalizability. Second, the cross-sectional design does not capture changes over time. Future research may consider these limitations and apply frameworks such as Theory of Planned Behavior or Value Belief Norm Theory.
Authors Contribution: CJPJ contributed to the study design, survey design, data collection, data analysis, writing and editing. HÇ contributed to the data analysis, writing, and editing. DA supervised the study, provided academic guidance, and contributed to manuscript revision. All authors read and approved the final manuscript.
Funding: This manuscript did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Competing interests: The authors declare no competing interests.
REFERENCES
Ajzen, I. (1991). The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2): 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Alonso, M.E., J.R. González-Montaña and J.M. Lomillos (2020). Consumers’ concerns and perceptions of farm animal welfare. Animals 10(3): 385. https://doi.org/10.3390/ani10030385
Ammann, J., G. Mack, N. El Benni, S. Jin, P. Newell-Price, S. Tindale and L.J. Frewer (2024). Consumers across five European countries prioritise animal welfare above environmental sustainability when buying meat and dairy products. Food Qual. Prefer. 117: 105179. https://doi.org/10.1016/j.foodqual.2024.105179
Arsoy, D. (2020). Herd management and welfare assessment of dairy goat farms in Northern Cyprus by using breeding, health, reproduction, and biosecurity indicators. Trop Anim Health Prod 52: 71–78 (2020). https://doi.org/10.1007/s11250-019-01990-3
Arsoy, D. (2022). One Welfare and One Health. In: R. Taştan, A. Ak, A. Peker and B. Küçük Biçer (eds.), One Health: Confronting the Complex Health Challenges of the 21st Century. Duvar Publications; İzmir (Türkiye). ISBN: 978-625-6945-00-5. 468 p.
Arsoy, D. (2023). The importance of animal breeding systems in healthy animal food production. In: O. Yılmaz (ed.), Veterinary Public Health in All Aspects. 1st Ed. Türkiye Klinikleri Publications; Ankara. pp. 113–116.
Arsoy, D. and A.İ. Uygun (2023). The effect of climate change on animal health and its evaluation with a One Health approach. In: R. Taştan, A. Ak, B. Küçük Biçer and D. Arslanbaş (eds.), Current One Health: Evolution of Thought from Zoonotic Threats to Climatic Disasters and Ecotoxicological Problems. 1st Ed. Duvar Publications; İzmir (Türkiye). ISBN: 978-625-6643-09-3. 472 p.
Baptiste, K.E. and N.C. Kyvsgaard (2017). Do antimicrobial mass medications work? A systematic review and meta-analysis of randomised clinical trials investigating antimicrobial prophylaxis or metaphylaxis against naturally occurring bovine respiratory disease. Pathogens and Disease 75(7): ftx083. https://doi.org/10.1093/femspd/ftx083
Barahona-Domínguez, L., J. An, B. Baker-Cook and S. Cho (2025). US consumer perception survey of animal welfare in broiler stunning. Front. Anim. Sci. 6: 1620566. https://doi.org/10.3389/fanim.2025.1620566
Bennett, R.M. and R.J. Blaney (2003). Estimating the impacts of animal disease through direct and indirect production losses. J. Agric. Econ. 54(1): 1–20. https://doi.org/10.1111/j.1477-9552.2003.tb00044.x
Bernués, A., A.M. Olaizola and K. Corcoran (2003). Labelling information demanded by European Union consumers regarding beef and lamb quality. Meat Sci. 65(3): 1095–1106. https://doi.org/10.1016/S0309-1740(02)00327-3
Blokhuis, H.J., R.B. Jones, R. Geers, M. Miele and I. Veissier (2003). Measuring and monitoring animal welfare: transparency in the food product quality chain. Animal Welfare 12(4): 445–455. https://doi.org/10.1017/S096272860002604X
Bonett, D.G. and T.A. Wright (2000). Sample size requirements for estimating Pearson, Kendall and Spearman correlations. Psychometrika 65(1): 23–28. https://doi.org/10.1007/BF02294183
Bozzo, G., M. Corrente, G. Testa, G. Casalino, M.M. Dimuccio, E. Circella, N. Brescia, R. Barrasso and F.E. Celentano (2021). Animal welfare, health and the fight against climate change: One solution for global objectives. Agriculture 11(12): 1248. https://doi.org/10.3390/agriculture11121248
Broom, D. M. and Fraser, A. F. (2015). Domestic animal behaviour and welfare. 5th Ed. CABI; Wallingford. 462 p.
Carrington, M.J., B.A. Neville and G.J. Whitwell (2010). Why ethical consumers don’t walk their talk: Towards a framework for understanding the gap between the ethical purchase intentions and actual buying behaviour of ethically minded consumers. J. Bus. Ethics 97(1): 139–158. https://doi.org/10.1007/s10551-010-0501-6
Chang, M.Y. and H.S. Chen (2022). Consumer attitudes and purchase intentions in relation to animal welfare-friendly products: Evidence from Taiwan. Nutrients 14(21): 4571. https://doi.org/10.3390/nu14214571
Chang, M.Y., C.T. Chao, J.H. Chen and H.S. Chen (2026). Seafood not from the sea: Examining consumer behavioral intentions toward plant-based seafood. Front. Nutr. 13: 1782036. https://doi.org/10.3389/fnut.2026.1782036
Cheftel, J.C. (2005). Food and nutrition labelling in the European Union. Food Chem. 93(3): 531–550. https://doi.org/10.1016/j.foodchem.2004.11.041
Chi, X., S. Kim, A.Y. Chiriko, H. Han, X. Cheng, B. Meng and J.J. Kim (2026). Tourists’ ethically responsible participation in animal-based tourism: A configurational impact assessment. J. Vacat. Mark. 32(1): 124–146. https://doi.org/10.1177/13567667241268650
Churchill, G.A. (1995). Marketing research: Methodological foundations. 6th Ed. Dryden Press; Fort Worth.
Civero, G., G. Punzo and D. Scarpato (2025). Exploring sustainable diet drivers: An extended TPB approach to alternative protein acceptance in Southern Italy. Nutrients 17(24): 3942. https://doi.org/10.3390/nu17243942
Clark, B., G.B. Stewart, L.A. Panzone, I. Kyriazakis and L.J. Frewer (2016). A systematic review of public attitudes, perceptions and behaviors towards production diseases associated with farm animal welfare. J. Agric. Environ. Ethics 29(3): 455–478. https://doi.org/10.1007/s10806-016-9615-x
Çelik, H. and A. Gül (2023). Consumer Perceptions and Purchase Intentions towards Organic Foods: Evidence from Eastern Mediterranean Region of Türkiye. Asian J. Agri. Ext. Econ. Sociol. 41 (11): 275-91. https://doi.org/10.9734/ajaees/2023/v41i112285
Çelik, H., Ş. Güler, D. Arsoy and Ö. Özden (2026). Is Eco-Friendly Meal Packaging a Priority for University Students in Northern Cyprus?: A Two-Stage Hybrid Study Using Structural Equation Modeling and Discrete Choice Experiment. New Medit. 26(1):109-127. https://doi.org/10.30682/nm2601g
Daley, C.A., A. Abbott, P.S. Doyle, G.A. Nader and S. Larson (2010). A review of fatty acid profiles and antioxidant content in grass-fed and grain-fed beef. Nutr. J. 9(1): 10. https://doi.org/10.1186/1475-2891-9-10
De Passillé, A.M. and J. Rushen (2005). Food safety and environmental issues in animal welfare. Rev. Sci. Tech. Off. Int. Epiz. 24(2): 757–766. https://pubmed.ncbi.nlm.nih.gov/16358525/
European Commission, Directorate-General for Health and Food Safety (2015). Study on the Evaluation of Regulation (EC) No. 178/2002 (“the General Food Law Regulation”): Final Report. Prepared by the Food Chain Evaluation Consortium; Brussels.
Emerson, R.W. (2015). Convenience sampling, random sampling, and snowball sampling: How does sampling affect the validity of research? J. Vis. Impair. Blind. 109(2): 164–168. https://doi.org/10.1177/0145482X1510900215
Essiz, O., S. Yurteri, C. Mandrik and A. Senyuz (2023). Exploring the value-action gap in green consumption: Roles of risk aversion, subjective knowledge, and gender differences. J. Glob. Mark. 36(1): 67–92. https://doi.org/10.1080/08911762.2022.2116376
Field, A. (2024). Discovering Statistics Using IBM SPSS Statistics. 6th Ed. SAGE Publications Ltd; London. 1144 p.
FAO (Food and Agriculture Organization of the United Nations) (2018). Sustainable Food Systems: Concept and Framework. Food and Agriculture Organization of the United Nations; Rome. 8 p. Available at: https://www.fao.org/3/ca2079en/CA2079EN.pdf. Accessed on 7 May 2026.
Font-i-Furnols, M. and L. Guerrero (2014). Consumer preference, behavior and perception about meat and meat products: An overview. Meat Sci. 98(3): 361–371. https://doi.org/10.1016/j.meatsci.2014.06.025
George, D. and P. Mallery (2021). IBM SPSS Statistics 27 Step by Step: A Simple Guide and Reference. 17th Ed. Routledge; New York. 418 p. https://doi.org/10.4324/9781003205333.
Gerber, P.J., H. Steinfeld, B. Henderson, A. Mottet, C. Opio, J. Dijkman, A. Falcucci and G. Tempio (2013). Tackling Climate Change through Livestock: A Global Assessment of Emissions and Mitigation Opportunities. Food and Agriculture Organization of the United Nations (FAO); Rome. 115 p.
Grandin, T. (1993). Behavioural agitation during handling of cattle is persistent over time. Appl. Anim. Behav. Sci. 36(1): 1–9. https://doi.org/10.1016/0168-1591(93)90094-6
Grgić, A., M. Šešum, M. Vekić, N. Jalić and A. Ostojić (2025). Attitudes of young consumers toward farm animal welfare. AgroReS 14: 92– 99. https://doi.org/10.63356/agrores.2025.012
Hewson, C.J. (2003). What is animal welfare? Common definitions and their practical consequences. Can. Vet. J. 44(6): 496–499.
International Dairy Federation (IDF) (2008). Guide to Good Animal Welfare in Dairy Production. International Dairy Federation; Brussels. ISBN: 978-9-2902980-41-4.
Kantor, B.N. and J. Kantor (2021). Public attitudes and willingness to pay for cultured meat: A cross- sectional experimental study. Front. Sustain. Food Syst. 5: 594650. https://doi.org/10.3389/fsufs.2021.594650
Kruskal, W. H. and W. A. Wallis (1952). Use of ranks in one-criterion variance analysis. J. Amer. Stat. Association 47(260): 583-621. https://doi.org/10.1080/01621459.1952.10483441
Kuchler, F., M. Sweitzer and C. Chelius (2023). The prevalence of the “natural” claim on food product packaging. EB-35. U.S. Department of Agriculture, Economic Research Service; Washington, DC.
Lassen, J., P. Sandøe and B. Forkman (2006). Happy pigs are dirty! Conflicting perspectives on animal welfare. Livestock Sci. 103(3): 221–230. https://doi.org/10.1016/j.livsci.2006.05.008
Lim, S.H. and E.T. Page (2022). Consumers’ interpretation of food labels with production claims can influence purchases. Amber Waves. U.S. Department of Agriculture, Economic Research Service; Washington, DC.
Mann, H. B. and D. R. Whitney (1947). On a test of whether one of two random variables is stochastically larger than the other. The Ann. Math. Stat. 18(1): 50–60. http://www.jstor.org/stable/2236101
Mench, J.A. (2008). Farm animal welfare in the USA: Farming practices, research, education, regulation, and assurance programs. Appl. Anim. Behav. Sci. 113(4): 298–312.https://doi.org/10.1016/j.applanim.2008.01.009
Mendoza, M.C.O., J.C.D. Chico, A.K.S. Ong and R.A.M. Regayas (2024). Assessment of Health Values, Beliefs, Norms, and Behavior towards Consumption Intention of 3D-Bioprinted Meat. Foods. 13(17): 2662. https://doi.org/10.3390/foods13172662
Napolitano, F. (2008). Effect of information about animal welfare on consumer willingness to pay for yogurt. J. Dairy Sci. 91(3): 910–917. https://doi.org/10.3168/jds.2007-0709
Ndou, S.P., V. Muchenje and M. Chimonyo (2011). Animal welfare in multipurpose cattle production systems and its implications on beef quality. Afr. J. Biotechnol. 10(7): 1049– 1064. https://doi.org/10.5897/AJB10.843
Nunnally, J.C. (1978). An overview of psychological measurement. In: B.B. Wolman (ed.), Clinical Diagnosis of Mental Disorders. Springer; Boston, MA. pp. 97–146.
Pasquariello, R., M. Bianchi, M. Capasso and D. Caso (2025). Meat, without meat: A TPB and VBN integration to understand intention to consume new generation of plant-based meat alternatives. British Food Journal 127(12): 4653–4672. https://doi.org/10.1108/BFJ-12-2024-1280
Pinillos, R.G., M.C. Appleby, X. Manteca, F. Scott-Park, C. Smith and A. Velarde (2016). One Welfare – a platform for improving human and animal welfare. Veterinary Record 179(16): 412–413. https://doi.org/10.1136/vr.i5470
Razali, N.M. and Y.B. Wah (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J. Stat. Model. Anal. 2(1): 21–33.
Richter, S., H. Stolz, A.L. Martinez-Cruz and A. Kachi (2025). Animal welfare has priority: Swiss consumers’ preferences for animal welfare, greenhouse gas reductions and other sustainability improvements in dairy products. Food Qual. Prefer. 123: 105350. https://doi.org/10.1016/j.foodqual.2024.105350
Ritchie, H. and M. Roser (2019). Half of the world’s habitable land is used for agriculture. Our World in Data. Available at: https://archive.ourworldindata.org/20251125-173858/global-landfor-agriculture.html. Accessed on 7 May 2026.
Sestino, A., M.V. Ross, L. Giraldi and F. Faggioni (2023). Innovative food and sustainable consumption behaviour: the role of communication focus and consumer-related characteristics in lab-grown meat (LGM) consumption. British Food Journal, 125(8): 2884–2901. https://doi.org/10.1108/BFJ-09-2022-0751
Stern, P.C., T. Dietz, T. Abel, G.A. Guagnano and L. Kalof (1999). A value-belief-norm theory of support for social movements: The case of environmentalism. Hum. Ecol. Rev. 6(2): 81–97. https://www.jstor.org/stable/24707060.
Wolf, C. A. and G. T. Tonsor (2017). Cow welfare in the U.S. dairy industry: Willingness-to-pay and willingness-to-supply. J. Agri. Resource Econ. 42(2): 164–179. http://www.jstor.org/stable/44329748
Wang, S.T. and S.C. Chang (2026). Influence of altruistic and biospheric values on vegetarians’ intentions to repurchase plant-based foods. Br. Food J. https://doi.org/10.1108/BFJ-08-2025-1080
Wilks, M. and C.J. Phillips (2017). Attitudes to in vitro meat: A survey of potential consumers in the United States. PLoS One 12(2): e0171904. https://doi.org/10.1371/journal.pone.0171904
Willett, W., J. Rockström, B. Loken, M. Springmann, T. Lang, S. Vermeulen, T. Garnett, D. Tilman, F. DeClerck, A. Wood, M. Jonell, M. Clark, L.J. Gordon, J. Fanzo, C. Hawkes, R. Zurayk, J.A. Rivera, W. De Vries, L. Sibanda, A. Afshin, A. Chaudhary, M. Herrero, P. Agustina, F. Branca, A. Lartey, S. Fan, R. Crona, E. Fox, V. Bignet, M. Troell, T. Lindahl, S. Singh, S.E. Cornell, K.S. Reddy, S. Narain, S. Nishtar and C.J.L. Murray (2019). Food in the Anthropocene: The EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet 393(10170): 447–492. https://doi.org/10.1016/S0140-6736(18)31788-4
WHO (World Health Organization) (2015). Global action plan on antimicrobial resistance. WHO; Geneva.
WHO (World Health Organization) (2017). Antibiotic resistance. WHO; Geneva.
WOAH (World Organisation for Animal Health) (2019). Terrestrial animal health code. WOAH; Paris.