Ethical and Responsible Food Purchasing Decisions of Consumers Within the Scope of Sustainable Food Policies: A Case Study of Istanbul Province

Abstract

This study examines consumers’ ethical and responsible food purchasing decisions in Istanbul Province, Türkiye. These decisions are crucial for sustainable food consumption and policies. The data for this study were collected through a survey of 616 individuals who are influential in food consumption decisions within their households in Istanbul. Factor analysis was conducted to identify the factors influencing food purchasing decisions among consumers. After conducting factor analysis on food purchasing decisions, eight subgroups were identified: environmentalism, economy, conservatism, diligence, innovativeness, informativeness, caring, and transformativeness. After the factor analyses, the differences and similarities in the factors considered in food purchasing decisions were analyzed. According to the results of the analyses, the demographic characteristics and socio-economic status (SES) group were found to be different. In this study, logit analysis was also employed to identify the profiles of conscious consumers in their food purchasing decisions. It was determined that 51.00% of consumers were conscious of their food purchasing decisions, and women were more conscious of these decisions than men, with women having higher age and educational status than men. Consumers’ food purchasing decisions were analyzed, and recommendations are presented for decision-makers regarding sustainable food policy, with the aim of providing information and raising awareness.

1. Introduction

Today, a significant proportion of consumers reside in urban areas, and most are removed from the realities of food production processes. Consumers are not sufficiently aware of the actions they can take to promote sustainable food consumption and their role in contributing to sustainable food policies. This situation requires consumers to be more informed about the environmental, economic, purchasing, social, and other aspects of food and to raise awareness of what they can do as consumers to reduce negative impacts.

Sustainable food consumption encompasses food-conscious purchasing behaviors, such as promoting sustainable, nutritious diets that protect individuals’ health; understanding environmental, economic, and social values regarding future generations; and enabling the ethical and responsible consumption of food.

Conscious consumption is the ethical purchasing process by which an individual chooses the most suitable product among alternatives, determines their genuine needs, and selects a product that does not harm society and the environment [1]. Consumers are aware of and can choose products whose production causes the least environmental damage or those produced by companies that care the most about the environment.

The ethical consumption of food and responsible food consumption complement each other and are among the most important components of sustainable food policies, as they support sustainable food consumption. Responsible food consumption involves considering the negative environmental, economic, and social impacts, as well as ethical values, in food purchasing behaviors. Food ethics is a set of ethical principles, moral values, and behaviors provided by moral norms and adopted by health sciences professional organizations [2].

Food ethics directs people to think more carefully, comprehensively, and systematically about existing and potential ethical problems in the food system [3]. Therefore, the ethical consumption of food can be explained by food purchasing behaviors that are sensitive to environmental, economic, and social justice.

Consumers developing a sense of ethics and responsibility, knowing what they can do for sustainable food consumption, and giving importance to this in their food purchasing decisions should be the aim. For decision-makers, understanding consumers’ food purchasing decisions is crucial for informing education, information, and awareness-raising activities that can be provided to consumers.

Consumer information and awareness-raising activities are expected to contribute to the strengthening and sustainability of food systems in the context of multifaceted issues, such as preventing food waste in society, by directing consumer decisions towards ethical and responsible consumption, ensuring efficient use of water, reducing the negative impacts of food on the environment, increasing the utilization of food, changing understanding of nutrition, ensuring the awareness of food trends in society, etc.

The responsibilization of consumption behaviors in daily life can improve the relationship between the state and citizenship, leading to a change in the responsible citizen lifestyle desired by states. Policies such as green consumption can be de-individualized and spread among wider groups [4,5]. Supporting this by creating a fair-trade environment could provide an opportunity to revitalize the desired ethical consumption policies [6].

In this study, ethical and responsible food purchasing decisions in Istanbul Province were analyzed, and recommendations were made to encourage awareness of ethical and responsible consumption and sustainable food practices.

2. Literature Review

Studies on the ethical, responsible, and sustainable consumption of food have been analyzed. Korkmaz and Sertoğlu [7] revealed that the important indicator of intention to purchase sustainable products is attitude. Yalım and Taluğ [3] emphasize the importance of explaining food ethics to consumers. Demirbaş [8] stated that non-governmental organizations play a dominant role in efforts to prevent food waste. Gürler and Nart [9] stated that consumers’ perceptions of health and a healthy lifestyle are variables that explain attitudes towards sustainable and healthy consumption. Kurtgil and Beyhan [10] stated that although it is a new concept, “sustainable nutrition” attracts attention due to its low environmental impact, meeting the nutritional needs of current and future generations as well as contributing to food and nutritional security. Haznedar and Aktaş [11] find it important to increase the level of food and nutrition literacy by including the sub-headings of sustainability and food security. Kadıoğlu and Sökülmez-Kaya [12] concluded that societies should first raise their awareness through education and then encourage action. Gökçe [13] states that consumers can support healthier, more nutritious, and cleaner foods by purchasing from local producers, thereby protecting small local producers who are often oppressed under the hegemony of international producers and supply chains. Ülkübaş-Tüzen [14] aims to analyze sustainability education, sustainable food systems education, and design thinking approaches and transform the theories in these approaches into a practical learning procedure for sustainable food systems education in K-12 education based on design thinking principles.

It is understood that the concepts of ethical, responsible, and sustainable food consumption are very new. As can be seen from the examples from Turkey mentioned in the preceding paragraph, there are research studies in the world that mostly examine consumer decisions on green consumption and other environmental impacts of consumption [15,16]. Some studies argue that the development of environmental awareness can lead to green consumption, as well as the development of nutritional awareness, which can reduce food waste [17]. The importance of understanding and encouraging ethical and responsible consumption in society, as well as the need for more quantitative research on these issues, is recognized.

Following the literature review, this study aims to explain consumers’ food purchasing decisions in terms of ethical and responsible consumption, encompassing food safety, the environmental impact of food, and the economic dimension of food. In this case, it is predicted that the results and recommendations of this research may be important for encouraging ethical and responsible consumption.

Studies in various countries, as documented in the literature, demonstrate that food security efforts can yield positive outcomes in production, consumption, intermediation, or other areas, in both developed and developing countries, as well as in regions with dense urbanization or rural populations, and in areas with diverse cultural practices [18,19,20,21,22,23,24,25,26,27,28]. This study is significant in that it reveals the ethical and responsible food consumption awareness of consumers in Istanbul, a rapidly developing city with increasing urbanization that hosts people from diverse demographic characteristics and receives migration from various regions of Türkiye.

In the research, it is argued that consumers’ food purchasing decisions (in terms of ethical and responsible consumption) will differ in terms of the following aspects: health, food consumption affecting the environment, and food economics. It is also argued that demographic characteristics, such as gender, age, education level, household income level, and SES group, have a significant effect on decisions. Based on this, the following research hypotheses were established:

Hypothesis 1.

Food purchasing decisions among consumers in terms of ethical and responsible consumption differ significantly under the influence of a wide range of factors.

Hypothesis 2.

There is a significant difference in food purchasing decisions in terms of ethical and responsible consumption between demographic characteristics such as gender, age, education level, household income level, and SES group.

3. Materials and Methods

3.1. Material

In this research, primary data were collected from people who are influential in household food purchasing decisions in Istanbul. The questionnaires were prepared to enable analyses of the data obtained. They were conducted by a professional survey company, which was selected in advance based on a calculation of the demographic distribution, under the guidance of the researcher. Secondary data involved a literature review and analysis of documents and reports on the food purchasing decisions of consumers, sustainable food consumption, and food policies.

3.2. Methods

3.2.1. Data Collection

Primary data collection involved surveys that were conducted in 9 districts (Kadıköy, Pendik, Üsküdar, Beşiktaş, Beşiktaş, Fatih, Sarıyer, Küçükçekmece, Arnavutköy, and Bakırköy) between January 2024 and May 2024 in Istanbul. In order to understand the demographic structure, the province’s population, gender distribution, income distribution, age distribution, socio-economic status (SES) groups, etc., were analyzed. In order to understand who constituted the SES groups, the research conducted by Kantar Media TNS (2012) was analyzed in detail, and the updated version was used [29]. In selecting the districts, it was taken into consideration that a total of 9 districts in the province, from 3 different regions and 3 subcategories (high, medium, and low), have consumers from various SES groups. For the surveys, a total of 616 randomly selected people who are influential in household food purchasing decisions were interviewed.

3.2.2. Sampling

Since the research was conducted in Istanbul, which has a known population, a finite sample was used. In addition, the answers given to the questions were in the form of yes–no, and a Likert scale required the use of formulae to estimate the rate. For this reason, 50% yes (0.50) and 50% (0.50) no rates were used in the formula. When the sample size was calculated with a 95% confidence limit and a 0.04 margin of error, a total of 601 questionnaires was reached.

The formula used to determine the sample size is as follows [30]:

$$\mathrm{n}={\displaystyle \frac{\mathrm{N}·\mathrm{p}·\mathrm{q}}{\left(\mathrm{N}-1\right)·\mathrm{D}+\mathrm{p}·\mathrm{q}}}$$

(1)

$$\mathrm{D}={\left({\displaystyle \frac{\mathrm{E}}{\mathrm{Z}}}\right)}^2={\left({\displaystyle \frac{0.04}{1.96}}\right)}^2=0.000416$$

(2)

$$\mathrm{n}={\displaystyle \frac{\mathrm{15,665,924}·0.50·0.50}{\left(\mathrm{15,665,924}-1\right)·0.000416+0.50·0.50}}=601$$

(3)

N = main population (population of Istanbul Province: 15,665,924 [30]);

n = number of samples (number of consumers surveyed = 601);

p = (0.50) probability that the unit under investigation occurs in the main population;

q = (0.50) probability that the examined unit does not occur in the main population;

D = error rate, D = (E/Z)²;

E = 0.04 (margin of error);

Z = 1.96 (confidence limit) 95% confidence limit.

In this research, we calculated that there may be incorrect or incomplete questionnaires; therefore, we decided to conduct approximately 3% more questionnaires. Finally, it was determined that 616 questionnaires were fully completed and evaluated in the research.

3.2.3. Statistical Data Analysis

The form of the questionnaire in this study is as follows:

Section 1: Demographic questions: In order to get to know the participants, demographic issues such as gender, age, education level, occupation, number of people living in the household, with whom they live in the household, household income, household expenditure, household food expenditure, etc., were gathered in general terms, without including name/surname information. In order to understand the SES group, questions such as whether the person who brings the most income into the household (head of the household) is working or not, as well as his/her occupation and education level, were asked.

Section 2: In order to determine the factors considered in food purchasing decisions in terms of ethical and responsible consumption, 50 judgments were prepared to be answered by eliminating from a pool of more than 150 judgments in the form of the following: 1. Never, 2. Rarely, 3. Sometimes, 4. Frequently, and 5. Always (using a 5-point Likert scale). In this study, consumers’ responses to these statements were analyzed.

In the research, factor analysis was employed to identify the factors influencing food purchasing decisions. Factors obtained from factor analysis were subjected to reliability analyses.

• Factor Analysis

Factor analysis is a type of multivariate statistical analysis that enables data to be presented in a more meaningful and summarized form based on the relationships between the data [31]. The main purpose of factor analysis is to investigate the origin of the interdependence between variables [32]. Basically, it aims to transform inter-related data structures into new data structures (sets) that are independent from each other and smaller in size, classifying them by revealing the structure in the relationships between variables. Additionally, the aim is to identify measurable variables with high correlation to the factors defined in the analysis [33].

In this study, a Kaiser–Meyer–Olkin measure of sampling adequacy test was performed to test the suitability of the data for factor analysis. In addition, the Bartlett test of sphericity was also used to determine whether the survey results were suitable for factor analysis. Based on the test results, the data were used in the factor analysis since the results were found to be appropriate.

In the factor analysis conducted in the research, the statements provided by participants, along with the judgments prepared using a 5-point Likert scale, were used to measure the important factors that consumers consider when purchasing food products. The Likert-type scale is a hybrid scale type that combines ordinal and interval properties. These types of scales are actually ordinal scales. However, it is artificially assumed to have an interval scale feature, allowing researchers to perform advanced statistical analyses. In this subject, 50 judgments were separated, classified, and named.

• Reliability Analysis

A wide variety of scales, such as tests and questionnaires, which are expressed as measurement tools consisting of a certain number of questions related to the subject of interest, were developed for measuring various characteristics (such as decisions, attitudes, and knowledge of the population) or randomly selected sample units of the subject of research. Several key points should be considered when developing a reliable measurement tool. Some of these important points are that the questions that make up the scale are related, consistent, understandable, and sufficiently numerous to accurately reflect the research findings [34].

In this study, reliability analysis was performed on the scales obtained from factor analysis:

The factors that consumers pay attention to when purchasing food products were determined using factor analysis. Firstly, the reliability of the scale was generally analyzed. Secondly, the judgments in the 8 scales obtained were listed, and the reliability of each scale was analyzed separately.

• Mann–Whitney U and Kruskal–Wallis Tests

The data set was checked for normal distribution. If the data set was not normally distributed (non-parametric) and there were 2 independent variables in the data set, the Mann–Whitney U test was applied. For example, if the data are to be examined according to gender characteristics, 2 independent variables, such as male and female, can be mentioned. Thus, the Mann–Whitney U test is applied. In this study, the Mann–Whitney U test was used to determine the differences and similarities according to gender in food purchasing decisions.

If the data set was not normally distributed (non-parametric) and contained more than 2 independent variables, the Kruskal–Wallis test was applied. For example, if the data set is examined according to educational status and includes 3 variables, such as high school, bachelor’s degree, and master’s degree, then the number of variables exceeds 2, and the Kruskal–Wallis test can be applied. In this study, the Kruskal–Wallis test was used to examine the differences and similarities in terms of age distribution, education level, household income level, and SES group regarding the issues considered in food purchasing decisions.

• “Chi-Square (χ²)” Test

The Chi-square (χ²) test is frequently used in statistical research and is preferred due to its ease of application [34]. The chi-square (χ²) analysis method is used not only to determine relationships but also to identify differences between variables [35]. The “Chi-square (χ²)” analysis method is an analysis method that operates on frequency distributions. The fact that the two variables are independent of each other means that there is no relationship between them. The “Chi-square (χ²)” test is widely used to measure the independence of variables. The most important feature of the “Chi-square (χ²)” analysis method is the degree of freedom. As the degree of freedom increases, the “Chi-square (χ²)” test begins to resemble a normal distribution. In addition, since the “Chi-square (χ²)” value depends on the degree of freedom, the “Chi-square (χ²)” value increases as the number of observations in the analysis increases. As a result, the probability of detecting signs of significant differences also increases. In fact, the “Chi-square (χ²)” analysis method helps to determine whether there is a systematic relationship between two variables. Firstly, the “Chi-square (χ²)” analysis method was used to test whether the observed relationship between the variables in a crosstab was statistically significant.

In this study, to understand consumers’ food purchasing decisions, questions were asked in the form of a 5-point Likert scale, with responses ranging from 1 to 5. A one-sample test was used to examine them (p > 0.01 and p > 0.05). The Chi-square (χ²) test is one of the most widely used tests among non-parametric tests [36]. In the Chi-square (χ²) analysis, in which the significance of the relationship between variables is tested, the expected value for each pore should never be zero, and the number of pores with an expected value below five/of five should not exceed 20 percent of the total pores [37]. The significance of the Chi-square (χ²) test shows that there is a relationship between the two variables [38]. The Chi-square (χ²) technique was applied to the data obtained.

• Logit Analysis

Bivalent selection models assume that individuals choose between two alternatives, depending on their character. When we have information about the decisions of individuals and the choices they will make, an equation can be used to predict their out-of-sample choices. Since it is possible to make many assumptions about the probability structures of individuals regarding choices, alternative model specifications emerge [39]. In this study, logit analysis—a multivariate statistical analysis method—was used to identify the criteria that influence whether individuals are conscious in their food purchasing decisions. The logit model, which was created as an alternative to the probit model to solve the problems encountered in a linear probability model, is more attractive in practice and is more widely used. Although it is the same as the probit model in terms of its formation process, it differs from it in terms of the cumulative distribution function (BDF) on which it is based [40].

In this study, the state of being conscious in food purchasing decisions is the dependent variable. In the Likert scale type, 50 statements provided by participants to assess their judgments on a wide variety of subjects were analyzed, and those with scores above the average were identified as conscious consumers. The average score mentioned in this study is 3.66. Accordingly, the conscious consumers of the research (1) are the participants who scored 3.67 and above. The unconscious consumers (0) of this study were the participants who scored 3.66 and below. The independent variables of the logit model consist of gender, age distribution, educational level, household income level, and SES group. In this study, the dependent and independent variables used in logit analysis to determine conscious consumers in food purchasing decisions and their identification are shown in

Table 1. Identification of conscious consumers in food purchasing decisions: logit analysis (dependent and independent variables).

	Variables	Definition
Arguments	Gender	1 = male, 2 = female
	Age distribution	1 = 18–29, 2 = 30–39, 3 = 40–49, 4 = 50–59, 5 = 60 years and older
	Education status	1 = secondary school and below, 2 = high school/associate degree, 3 = bachelor’s/graduate school
	Household income level	1 = TRY 17,002 and below, 2 = between TRY 17,003 and 30,000, 3 = between TRY 30,001 and 50,000, 4 = between TRY 50,001 and 80,000, 5 = 80,001 TRY and above
	SES group	1 = A, 2 = B, 3 = C1, 4 = C2, 5 = D, 6 = E
Dependent variable	State of consciousness	1 = conscious consumer; 0 = unconscious consumer

.

With the general functional representation of logit substances, the individual’s conscious food purchasing decisions are

$$Pi=E\left(Y=1|x\right)={\displaystyle \frac{1}{1+e^{-({\beta }_1+{\beta }_2{X}_2\bigwedge +{\beta }_k{X}_k)} }}$$

(4)

or

$$P_i={\displaystyle \frac{1}{1+{\mathrm{e}}^{-\mathrm{z}}}}$$

(5)

Here,

$${Z_{i }=\beta }_1+{\beta }_2{X}_2\bigwedge +{\beta }_k{X}_k$$

(6)

and Equation (5) is known as the (cumulative) logistic distribution function. While Z_i varies in the range of −∞ to +∞, it is known that P_i takes values of between 0 and 1, and its relationship with Z_i is not linear. If the state of being conscious in the food purchasing decision is P_i, the probability of consciously determining food purchasing decisions (1 − P_i) is as follows:

$${1-P}_i={\displaystyle \frac{1}{1+e^{Z_i}}}$$

(7)

Therefore, it can be written that

$${\displaystyle \frac{P_i}{1-P_i}}={\displaystyle \frac{1+e^{Z_i}}{1+e^{-Z_i}}}=e^{Z_i}$$

(8)

P_i/(1 − P_i), in this case, is the betting odds of having information. If the natural logarithm of this equation is taken, the following conclusion is reached:

$$L_i=\ln \left({\displaystyle \frac{P_i}{1-P_i}}\right)=Z_i$$

(9)

$${=\beta }_1+{\beta }_2{X}_2\bigwedge +{\beta }_k{X}_k$$

(10)

The logarithm of the betting odds L is linear not only with respect to X but also with respect to the main mass coefficients. L is called logit, and the logit model comes from Equation (9) [41].

In this study, the factors used as explanatory variables in the binary logit analysis of the concept of a conscious consumer in food purchasing decisions are given as follows:

Ln[Pi/(1 − Pi)] = Yi = β0 + β1Gender + β2Age + β3EducationalStatus + β4HousehouldIncomeLevel + β5SESGroup

(11)

The Ln[Pi/(1 − Pi)] probability ratio (i.) indicates the probability that the consumer is conscious in their food purchasing decisions.

(12)

The model was estimated using the maximum likelihood method. Considering the results of these estimates, the probabilities and probability ratios of individuals being conscious in their food purchasing decisions were calculated. This method has many statistical features. All estimators are consistent and asymptotically active. The likelihood ratio (LR) test can be applied when the significance of all or some of the coefficients is tested in the logit model, estimated using the highest likelihood method [42]. In addition, in terms of the goodness of fit, the R² value is not considered a suitable measure for logit models [43]. Although many alternatives were proposed for the goodness of harmony, the Nagelkerke R² value was used.

The interpretation of the estimators of the logit model is not as easy as in the linear regression model. Odds ratios are used in the interpretation of these coefficients. The odds ratios are obtained by taking the exponential of the natural logarithms of the estimators’ coefficients. The odds ratio indicates how many times the probability of the dependent variable occurring is affected if the relevant independent variable takes a value of 1 (one) or 0 (zero), while the other variables remain constant. In addition, as a rule, if the regression coefficients have a negative value, the odds ratios of these coefficients should be corrected as OO = 1/OO [36].

4. Results

4.1. Survey Results

4.1.1. Demographic Analysis

In this study, the demographic structure and SES groups of the participants, such as gender, age distribution, marital status, educational status, number of people living in the household, with whom they live in the household, occupation, household monthly income, household monthly expenditures, and household monthly food expenditures, were determined (

Table 2. Distribution by demographic characteristics (n/%).

Variables	N	%	Variables	N	%
Gender			Education Status
Female	349	56.70	Illiterate	4	0.65
Male	267	43.30	Primary school	90	14.61
Marital Status			Secondary school	48	7.80
Married	464	75.32	High school	191	31.00
Celibate	123	19.97	Associate’s degree	45	7.31
Other	29	4.71	License	185	30.03
Age Distribution			Graduate	53	8.60
18–29	81	13.15	Households
30–39	223	36.50	Family	568	92.21
40–49	138	22.40	Consanguineous	8	1.30
50–59	95	15.42	Friend	4	0.65
60 and above	79	12.83	Alone	36	5.84
Distribution of Professions			Number of Residents in the Household
Nonoperating	52	8.44	1	36	5.85
Student	64	10.39	2	150	24.35
Self-employment	89	14.45	3	160	25.97
Public	109	17.70	4	167	27.11
Private	249	40.42	5	73	11.85
Retired	53	7.47	6 and up	30	4.87
Household Monthly Income			Household Monthly Expenditure
TRY 17,002 and below	99	16.07	TRY 10,000 and below	67	10.88
Between TRY 17,003 and 30,000	113	18.34	Between TRY 10,001 and 20,000	149	24.19
Between TRY 30,000 and 50,000	148	24.02	Between TRY 20,001 and 30,000	137	22.24
Between 50,001 TRY and 80,000 TL	127	20.62	Between TRY 30,001 and 50,000	129	20.94
80.001 TRY and above	83	13.48	TRY 50,001 and above	89	14.45
No answer	46	4.47	No answer	45	7.30
SES Group			Household Monthly Food Expenditure
A	66	10.71	TRY 10,000 and below	272	44.15
B	126	20.45	Between TRY 10,001 and 15,000	107	17.37
C1	161	26.14	Between TRY 15,001 and 20,000	93	15.10
C2	124	20.13	Between TRY 20,001 and 25,000	39	6.33
D	86	13.96	TRY 25,001 and above	69	11.20
E	53	8.61	No answer	36	5.85

). A total of 56.70% of the participants were female, and 43.30% were male. The average age of the participants was calculated as 42.30 years. A total of 13.15% of the participants were between the ages of 18 and 29, 36.20% were between 30 and 39, 22.40% were between 40 and 49, 15.42% were between 50 and 59, and 12.83% were aged 60 years and over. A total of 0.65% of the participants were illiterate, 14.61% were primary school graduates, 7.80% were secondary school graduates, 31% were high school graduates, 7.31% were associate degree graduates, 30.03% were undergraduate graduates, and 8.60% were postgraduate graduates. A total of 19.97% of the participants were single, 75.32% were married, and 4.71% were other. A total of 5.85% of the participants lived alone in their households. Those living with two people in the household totaled 24.35%, those living with three people totaled 25.97%, those living with four people totaled 27.11%, those living with five people totaled 11.85%, and those living with six people or more totaled 4.87%. A total of 92.20% of the participants lived with their families in the household. Living with relatives in the household constituted 1.30%, living with friends constituted 0.65%, and living alone constituted 5.84%. A total of 8.44% of the participants were not working, 10.39% were students, 8.60% were retired, 14.45% were self-employed, 17.70% worked in the public sector, and 40.42% worked in the private sector. The monthly household income of 16.07% of the participants was TRY 17,002 and below, 18.34% earned between TRY 17,003 and 30,000, 24.02% earned between TRY 30,001 and 50,000, 20.62% earned between TRY 50,001 and 80,000, and 13.48% earned TRY 80,001 and above. A total of 7.47% of the participants did not specify their household income. It was determined that 10.88% of the participants had a monthly household expenditure of TRY 10,000 or less, 24.19% spent between TRY 10,001 and 20,000, 22.24% spent between TRY 20,001 and 30,000, 20.94% spent between TRY 30,001 and 50,000, and 14,45% had a monthly household expenditure of TRY 50,001 and above. A total of 7.30% of the participants did not specify their monthly household expenditure, and 44.15% of the participants stated that their monthly expenditure was below TRY 10,000, 17.37% spent between TRY 10,001 and 15,000, 15.10% spent between TRY 15,001 and 20,000, 6.33% spent between TRY 20,001 and 25,000, and 11.20% had a monthly expenditure above TRY 25,001. A total of 5.85% of the participants did not answer this question. A total of 10.71% of the participants were in SES A, 20.45% in SES B, 26.14% in SES C1, 20.13% in SES C2, 13.96% in SES D, and 8.61% in SES E.

4.1.2. Factor Analysis

In this research, we aimed to identify the factors influencing the food purchasing decisions of the participants. For this purpose, the statements given about 50 judgments directed to the participants on the issues of safe food and the environmental and economic aspects of food purchasing decisions were factor analyzed. Participants’ food purchasing decisions were analyzed, and factors were determined. Afterwards, factor groups were separated, classified, and named according to the results obtained in the factor analysis.

In this study, the variables related to the participants’ food purchasing decisions were analyzed. Factor analysis was applied to render a large number of inter-related variables in food purchasing decisions as being few, meaningful, and independent of each other. The data obtained in this study were analyzed using the Kaiser–Meyer–Olkin measure of sampling adequacy test and Bartlett’s test of sphericity to assess their suitability for factor analysis. As a result of the analysis, the results of the KMO and Bartlett tests were obtained. The KMO test yielded a value of 0.957. A KMO value of more than 90.00% is considered a perfect fit. According to this, the high KMO ratio in this study shows compliance with factor analysis. As a result of the Bartlett test, the Chi-square (χ²) value was calculated as 16,628.03. Sd (degrees of freedom/df) was found to be 1225, and P (probability/sig.) was calculated as 0 (

Table 3. KMO and Bartlett test results of food purchasing decisions.

KMO and Bartlett Tests
Kaiser–Meyer–Olkin (KMO)		0.957
Bartlett sphericity test	χ2 (Chi-square)	16,628.03
	Sd (degrees of freedom)	1225
	P (probability)	0.000 *

* 0.01 Significance level.

). Since the values were found to be appropriate, the data were used in factor analysis.

At the beginning of the factor analysis, eigenvalues, variance, and cumulative variance values were used to determine the number of factors. Based on the results of the factor analysis of the variables related to the participants’ food purchasing decisions in general, eight factors were calculated. The cumulative variance of these eight factors was calculated as 58.897%. The results of the analysis explaining the total variance of the food purchasing decisions scale are presented in

Table 4. Explanation of the total variance of the food purchasing decisions scale.

Factor (Component)	Initial Eigenvalues			Extraction Total			Total Factor Loads (Rotated)
	Total	Variance Explained (%)	Cumulative (%)	Total	Variance Explained (%)	Cumulative (%)	Total	Variance Explained (%)	Cumulative (%)
1	17.569	35.138	35.138	17.569	35.138	35.138	8.203	16.406	16.406
2	2.91	5.82	40.958	2.91	5.82	40.958	4.151	8.302	24.708
3	2.617	5.233	46.192	2.617	5.233	46.192	4.114	8.227	32.935
4	1.702	3.404	49.595	1.702	3.404	49.595	4.086	8.172	41.108
5	1.339	2.678	52.273	1.339	2.678	52.273	3.684	7.368	48.475
6	1.221	2.442	54.715	1.221	2.442	54.715	2.14	4.280	52.755
7	1.057	2.113	56.828	1.057	2.113	56.828	1.631	3.262	56.017
8	1.034	2.069	58.897	1.034	2.069	58.897	1.44	2.88	58.897
9	0.953	1.906	60.803
10	0.915	1.83	62.633
11	0.881	1.762	64.395
12	0.848	1.696	66.09
13	0.769	1.537	67.628
14	0.765	1.53	69.158
15	0.701	1.402	70.56
16	0.682	1.364	71.924
17	0.68	1.359	73.284
18	0.669	1.338	74.621
19	0.657	1.314	75.935
20	0.632	1.264	77.199
21	0.585	1.17	78.369
22	0.564	1.128	79.497
23	0.549	1.097	80.594
24	0.54	1.081	81.675
25	0.523	1.046	82.721
26	0.496	0.991	83.713
27	0.49	0.98	84.693
28	0.468	0.937	85.629
29	0.465	0.93	86.559
30	0.454	0.909	87.468
31	0.441	0.882	88.35
32	0.416	0.832	89.182
33	0.401	0.801	89.983
34	0.393	0.786	90.769
35	0.384	0.768	91.537
36	0.368	0.736	92.273
37	0.354	0.708	92.981
38	0.352	0.704	93.685
39	0.334	0.667	94.352
40	0.309	0.618	94.97
41	0.303	0.607	95.577
42	0.3	0.599	96.176
43	0.28	0.561	96.737
44	0.274	0.549	97.286
45	0.257	0.515	97.801
46	0.246	0.492	98.293
47	0.232	0.464	98.757
48	0.227	0.454	99.211
49	0.209	0.419	99.63
50	0.185	0.37	100

Extraction method: principal component analysis.

.

The results of the factor analyses related to the food purchasing decisions of the participants in this study are shown in

Table 5. Factor loadings of the food purchasing decisions scale.

No.	Judgments	Components (Factors)
		1	2	3	4	5	6	7	8
	1. Factor: Environmentalism
Ç2	I ensure that food packaging is not harmful to the environment.	0.747
Ç1	I care about environmental protection in food production processes.	0.746
Ç3	The presence of a recycling sign on food packaging influences my decision positively.	0.708
Ç4	I make sure to use recyclable packaging.	0.685
G16	I find expert opinions useful.	0.664
G15	I emphasize the importance of the state to establish traceable and auditable systems.	0.658
G10	I check the expiry date.	0.649
G17	I can update my food shopping list according to the information I have acquired.	0.635
G11	I check whether the packaging is intact and whether the packaging has been opened.	0.599
Ç7	I prefer to buy products that are certified to be environmentally protected in the production–distribution–manufacturing stages.	0.591
Ç5	I prefer food products whose production process has been monitored.	0.578
G12	I find organic and/or natural products healthy.	0.550
G8	I look at the label to see what kind of additives it contains.	0.493
G9	I know which additives can be harmful to my health.	0.490
Ç8	I prefer environmentally friendly products, even if they are expensive compared to similar products.	0.488
Ç6	I can understand the certifications established for the protection of the environment on food labels.	0.479
	2. Factor: Economy
E8	Sales-increasing campaigns, such as promotions and product campaigns, increase my willingness to buy.		0.769
E9	I prefer discounted products.		0.764
E7	I follow sales-increasing campaigns such as promotions and product campaigns.		0.746
E10	I am more willing to buy the food product that I see advertised.		0.585
E1	The fact that the food product is affordable positively affects my willingness to buy.		0.495
E15	I prefer grocery stores close to my home for food shopping.		0.489
E17	I find big supermarkets more economical.		0.485
E16	If it is affordable, I shop at markets with more distant locations.		0.471
	3. Factor: Conservatism
G4	I find the information written/located on food labels sufficient.			0.753
G5	I think that the information written on the food labels reflects the truth.			0.708
G6	I buy by calculating the nutritional value.			0.610
G7	I make it a point to eat hearty food.			0.533
E2	I can pay more for branded products even if they have the same features.			0.397
	4. Factor: Diligence
Ç14	I follow information activities regarding the environment.				0.642
Ç13	I take care not to waste water when washing food products.				0.632
Ç11	I make my shopping list carefully to avoid wasting food.				0.616
Ç12	I make sure to consume food products in the season in which they are grown.				0.589
Ç15	I care about activities that protect the environment.				0.578
E3	The fact that the food product I buy is locally produced positively affects my decision.				0.533
E11	I make a shopping list before I go shopping.				0.490
	5. Factor: Innovativeness
E6	I can pay more for geographically labeled or local products.					0.712
E5	Products with a geographical indication increase my willingness to buy.					0.625
E12	I can pay more for organic or natural products.					0.592
E18	I favor shopping in smaller local markets.					0.567
E4	I care about supporting local products.					0.460
E14	I shop from companies that care about the consumer.					0.417
E13	I will not shop again from companies that produce out of standard.					0.373
	6. Factor: Informativeness
G1	I always read the labels of food products.						0.662
G2	I can understand the information on the label of a food product.						0.558
G3	I can understand information about nutritional values (protein, fat, carbohydrate, content etc.).						0.493
	7. Factor: Caring
G14	I follow the state’s inspections of food companies.							0.545
G13	I call Alo 174 when I have problems with food.							0.544
	8. Factor: Transformativeness
Ç9	I find organic or natural products environmentally friendly.								0.727
Ç10	I separate packaging into glass, metal, plastic, paper, etc.								0.433

.

According to the results of the factor analysis, subgroups were identified, separated, classified, and named according to the characteristics of the items under the factor. Table 5 shows the items under each factor and the factor loadings of these items. “Factor 1: Environmentalism” consists of 16 items, and the factor loadings of the items in this dimension vary between 0.747 and 0.479. “Factor 2: Economy” consists of eight items, and the factor loads of the items in this dimension vary between 0.769 and 0.471. “Factor 3: Conservatism” consists of five items, and the factor loads of the items in this dimension vary between 0.753 and 0.397. “Factor 4: Diligence” consists of seven items, and the factor loadings of the items in this dimension vary between 0.642 and 0.490. “Factor 5: Innovativeness” consists of seven items, and the factor loads of the items in this dimension vary between 0.712 and 0.373. “Factor 6: Informativeness” consists of three items, and the factor loads of the items in this dimension vary between 0.662 and 0.493. “Factor 7: Caring” consists of two items, and the factor loadings of the items in this dimension vary between 0.545 and 0.544. “Factor 8: Transformationalism” consists of two items, and the factor loads of the items in this dimension vary between 0.727 and 0.433 (Table 5).

The characteristics of each factor and the explanation of food purchasing decisions are as follows:

• “Factor 1: Environmentalism” is the factor in which the decisions about environmental issues are considered in food purchasing decisions. Variance was calculated as 16.406%. According to this, the environmentalism factor explains 16.406% of food purchasing decisions.
• “Factor 2: Economy” is the factor in which the decisions about issues related to an individual’s economy are considered in food purchasing decisions. Variance was calculated as 8.302%. Accordingly, the economic factor explains 8.302% of the food purchasing decisions.
• “Factor 3: Conservatism” explains the effect of more conservative tendencies on food purchasing decisions. Variance was calculated as 8.227%. Accordingly, the conservatism factor explains 8.227% of food purchasing decisions.
• “Factor 4: Diligence” explains the effect of more careful decisions on food purchasing decisions. Variance is calculated as 8.172%. Accordingly, the caring factor explains 8.172% of food purchasing decisions.
• “Factor 5: Innovativeness” explains the effect of more advanced and innovative attitudes on food purchasing decisions. Variance was calculated as 7.368%. Accordingly, the innovativeness factor explains 7.368% of food purchasing decisions.
• “Factor 6: Informativeness” explains the effect of decisions developed by feeling more informed in food purchasing decisions. Variance was calculated as 4.28%. Accordingly, the informativeness factor explains 4.28% of food purchasing decisions.
• “Factor 7: Caring” explains the effect of decisions made by being more relevant in food purchasing decisions. Variance was calculated as 3.262%. Accordingly, the relevance factor explains 3.262% of the food purchasing decisions.
• “Factor 8: Transformativeness” explains the effect of decisions made with an attitude of wanting more innovation in food purchasing decisions. Variance is calculated as 2.88%. Accordingly, the transformativeness factor explains 2.88% of food purchasing decisions.

In

Table 6. Reliability test of factor analysis of food purchasing decisions.

No.	Scales	Article Count	Cronbach’s Alpha	Scale Reliability
	Food purchasing decisions (general)	50	0.960	Highly reliable
1	Environmentalism	16	0.938	Highly reliable
2	Economy	8	0.840	Highly reliable
3	Conservatism	5	0.759	Very reliable
4	Diligence	7	0.854	Highly reliable
5	Innovativeness	7	0.841	Highly reliable
6	Informativeness	3	0.769	Very reliable
7	Caring	2	0.674	Very reliable
8	Transformativeness	2	0.593	Low reliability

, the results of the reliability tests and the suitability of the factor analysis of food purchasing decisions are given.

As a result of the reliability analysis, the reliability of the food purchasing decisions evaluation scale, comprising 50 items, was evaluated. The Cronbach’s alpha value for the relationship between the scale questions in evaluating food purchasing decisions is generally highly reliable, with a value of 0.960. Looking at the subgroups, the relationship between the scale questions was found to be “highly reliable” for Factor 1: Environmentalism, Factor 2: Economy, Factor 4: Caring, and Factor 5: Innovativeness. Factor 3: Conservatism, Factor 6: Informativeness, and Factor 7: Caring were found to be “highly reliable,” and only Factor 8: Transformativeness had a reliability that was calculated as “low,” with 0.593. The highest degree of reliability was calculated for Factor 1: Environmentalism, with 0.938 (Table 6).

The effect of gender on participants’ food purchasing decisions was analyzed. For this purpose, the eight factors obtained from the factor analysis were first listed in one column. In the other column, the sum of the judgments under each factor in general and by gender was divided by the total number of judgments, and the factor averages are shown in the columns in general and by gender. As a result of the analyses carried out for each factor in this study, it was understood that not all of the factors showed a normal distribution, so the Mann–Whitney U test, which is applied in cases with two independent variables (such as male and female) in non-normally distributed (non-parametric) data (

Table 7. Analysis of gender in food purchasing decisions.

Components (Factors)	Factor Averages			Type of Distribution	Made Test	Analysis Value	Analysis Result
	General	Gender
		Female	Male
Environmentalism	3.85	3.94	3.73	Non-parametric	Mann–Whitney U Test	0.004	Difference
Economy	3.49	3.51	3.47			0.901	Indifference
Conservatism	3.06	3.05	3.06			0.893	Indifference
Diligence	3.87	3.98	3.72			0.000	Difference
Innovativeness	3.73	3.80	3.64			0.036	Difference
Informativeness	3.74	3.80	3.67			0.222	Indifference
Caring	2.97	2.89	3.08			0.094	Indifference
Transformativeness	3.80	3.84	3.75			0.501	Indifference

), was used.

It was determined that gender is a significant factor in the environmentalism, caring, and innovativeness aspects of food purchasing decisions, and that there is a difference in food purchasing decisions based on gender. According to the research, women pay significantly more attention to environmentalism, caring, and innovativeness factors than men (Table 7).

The effect of age on the food purchasing decisions of the participants was analyzed. For this purpose, the eight factors obtained from the factor analysis were listed in one column. In another column, the sum of the judgments under each factor in general and according to age groups was divided by the total number of judgments; the factor averages are shown in the columns in general and according to age group. Since it was understood that not all of the factors showed a normal distribution as a result of the analyses performed for each factor in this study, the Kruskal–Wallis test, which is applied in cases with more than two independent variables (such as age groups) in non-normally distributed (non-parametric) data (

Table 8. Analysis of age in food purchasing decisions.

Components (Factors)	Factor Averages						Type of Distribution	Made Test	Analysis Value	Analysis Result
	General	Age Distribution
		18–29	30–39	40–49	50–59	60 and Up
Environmentalism	3.85	3.63	3.82	3.93	3.85	4.02	Non-parametric	Kruskal–Wallis Test	0.208	Indifference
Economy	3.49	3.26	3.47	3.52	3.56	3.67			0.159	Indifference
Conservatism	3.06	3.06	3.06	3.14	2.92	3.08			0.653	Indifference
Diligence	3.87	3.63	3.80	3.99	3.92	4.00			0.080	Indifference
Innovativeness	3.73	3.47	3.68	3.87	3.74	3.90			0.049	Difference
Informativeness	3.74	3.66	3.77	3.76	3.68	3.77			0.931	Indifference
Caring	2.97	2.81	3.03	3.03	2.82	3.07			0.482	Indifference
Transformativeness	3.80	3.56	3.80	3.84	3.78	3.97			0.144	Indifferent

), was used.

Among the factors affecting food purchasing decisions between age groups, a difference was found only in the innovativeness factor. While those under 40 pay less attention to the innovativeness factor, those aged 40 and above pay more attention to it. It was determined that those aged 60 and over pay the most attention to the innovativeness factor (Table 8).

The effect of educational status on the food purchasing decisions of the participants was analyzed. For this purpose, the eight factors obtained from the factor analysis were first listed in one column. In another column, the sum of the judgments under each factor according to general and educational status was divided by the total number of judgments; the factor averages are shown in the columns according to the distribution of general and educational status. In the research, since it was understood that not all of the factors showed a normal distribution as a result of the examinations carried out for each factor, the Kruskal–Wallis test, which is applied in cases with more than two independent variables (such as educational status) in non-normally distributed (non-parametric) data (

Table 9. Analysis of education status in food purchasing decisions.

Components (Factors)	Factor Averages				Type of Distribution	Made Test	Analysis Value	Analysis Result
	General	Education Status
		Secondary School and Below	High School and Associate Degree	Undergraduate and Postgraduate
Environmentalism	3.85	3.66	3.86	3.96	Non-parametric	Kruskal–Wallis Test	0.068	Indifference
Economy	3.49	3.55	3.48	3.47			0.618	Indifference
Conservatism	3.06	2.80	3.07	3.20			0.001	Difference
Diligence	3.87	3.78	3.85	3.94			0.463	Indifference
Innovativeness	3.73	3.61	3.75	3.78			0.335	Indifference
Informativeness	3.74	3.39	3.66	4.03			0.000	Difference
Caring	2.97	2.90	2.98	3.00			0.778	Indifference
Transformativeness	3.80	3.61	3.82	3.89			0.152	Indifference

), was used.

Based on the educational level, a difference was found in the conservatism and informativeness factors. It has been determined that individuals with a high school education or higher pay more attention to the conservatism factor than those with less than a high school education. It was found that bachelor’s and postgraduate graduates pay more attention to the informativeness factor compared to the others (Table 9).

The effect of income level on the food purchasing decisions of the participants was analyzed. For this purpose, the eight factors obtained from the factor analysis were first listed in a single column. In another column, the sum of the judgments under each factor in general and by income level was divided by the total number of judgments, and the factor averages are shown in the columns in general and by household income level. For each factor, the aim was first to determine the type of analysis to be performed on the data. Since it was understood that none of the factors in the research exhibited a normal distribution, the Kruskal–Wallis test, which is applied in cases with more than two independent variables (such as household income level) in non-parametric data (

Table 10. Analysis of household income level in food purchasing decisions.

Components (Factors)	Factor Averages						Type of Distribution	Made Test	Analysis Value	Analysis Result
	General	Income Status
		TRY 17,002 and Below	TRY 17,003–30,000	TRY 30,001–50,000	TRY 50,001–80,000	TRY 80,001 and Up
Environmentalism	3.85	3.82	3.90	3.87	3.88	3.89	Non-parametric	Kruskal–Wallis Test	0.961	Indifference
Economy	3.49	3.44	3.43	3.66	3.52	3.56			0.157	Indifference
Conservatism	3.06	2.93	2.88	3.11	3.41	3.33			0.001	Difference
Diligence	3.87	3.83	3.91	3.96	3.77	3.96			0.371	Indifference
Innovativeness	3.73	3.64	3.72	3.87	3.69	3.89			0.186	Indifference
Informativeness	3.74	3.68	3.60	3.83	3.87	3.96			0.055	Indifference
Caring	2.97	3.10	2.85	3.15	2.96	3.18			0.282	Indifference
Transformativeness	3.80	3.80	3.81	3.79	3.83	4.07			0.341	Indifference

), was employed.

Differences were found in the conservatism factor according to income status. It is understood that the conservatism factor receives more attention at household income levels above TRY 30,000. Those who paid the most attention to the conservatism factor were found to be those with a household income level of between TRY 50,001 and 80,000 (Table 10).

The effect of SES group on the food purchasing decisions of the participants was analyzed. For this purpose, the eight factors obtained from the factor analysis were first listed in a single column. In another column, the sum of the judgments under each factor in general and according to SES group was divided by the total number of judgments; factor averages are shown in the columns according to the distribution of general and SES group. For each factor, the aim was first to determine the type of analysis to be performed on the data. Since it was understood that not all of the factors in the research showed a normal distribution, the Kruskal–Wallis test, which is applied in cases with more than two independent variables (such as SES groups) in non-parametric data that do not show normal distribution, was used (

Table 11. Analysis of SES groups in food purchasing decisions.

Components (Factors)	Factor Averages							Type of Distribution	Made Test	Analysis Value	Analysis Result
	General	SES Groups
		A	B	C1	C2	D	E
Environmentalism	3.85	3.84	4.00	3.77	3.92	3.79	3.68	Non-parametric	Kruskal–Wallis Test	0.411	Indifference
Economy	3.49	3.36	3.66	3.42	3.60	3.48	3.27			0.077	Indifference
Conservatism	3.06	3.08	3.28	3.10	3.02	2.90	2.72			0.024	Difference
Diligence	3.87	3.93	3.97	3.81	3.90	3.81	3.72			0.835	Indifference
Innovativeness	3.73	3.71	3.89	3.69	3.82	3.61	3.51			0.190	Indifference
Informativeness	3.74	3.98	4.00	3.60	3.78	3.58	3.41			0.001	Difference
Caring	2.97	2.80	3.16	2.80	3.11	2.97	2.95			0.226	Indifference
Transformativeness	3.80	3.95	3.94	3.70	3.71	3.85	3.71			0.413	Indifference

).

Differences were found between SES groups in terms of the conservatism and informativeness factors. It is understood that higher SES groups, such as A, B, and C1, pay more attention to the conservatism factor compared to the C1, D, and E SES groups. Those in the B SES group were found to pay the most attention to the conservatism factor. As for the informativeness factor, it was found that those in SES groups B and A, followed by those in SES group C2, pay more attention to this factor than those in SES groups C1, D, and E (Table 11).

4.1.3. Logit Analysis

The research aims to determine the role of conscious consumers in food purchasing decisions. Despite the separated, classified, and renamed factors obtained from the factor analysis, the independent variables can generally reveal the most conscious consumer in food purchasing decisions. From this perspective, the aim is to identify the most conscious consumer in food purchasing by utilizing the previous research results. For this purpose, it was decided to use logit analysis, which is most commonly used in such cases.

For logit analysis, the dependent variable was a consumer who makes conscious decisions in food purchases. The independent variables were gender, age, education level, household income level, and SES group. Before conducting the logit analysis in this study, it was necessary to identify consumers who are conscious of their food purchasing decisions. Firstly, the factor averages obtained through factor analysis for each participant’s food purchasing decisions were determined. Then, they were summed with the eight factors obtained previously, and finally, the score of the participants was obtained by dividing by the number of factors. In this study, the average score of the participants was 3.66, and the standard deviation was 0.36. Those with a score of 3.66 and below were classified as unconscious consumers, and those with a score of 3.67 and above were classified as conscious consumers prior to the analyses. In this study, the participants’ awareness or unawareness of their food purchasing decisions in general, and according to the variables of gender, age distribution, educational status, household income level, and SES group, were examined.

• It was determined that 51% of the participants (314 people) made conscious food purchasing decisions, and 49% (302 people) made unconscious decisions.
• Among the participants, 54.20% of women and 46.80% of men were found to be conscious in their food purchasing decisions.
• It was determined that 39.50% of the participants between the ages of 18 and 29, 52.50% of the participants between the ages of 30 and 39, 57.20% of the participants between the ages of 40 and 49, 43.20% of the participants between the ages of 50 and 59, and 57% of the participants aged 60 and over were conscious in their food purchasing decisions.
• It was determined that 40.10% of the participants with an education level of secondary school and below, 52.10% of the participants with high school education and associate’s degrees, and 56.30% of the participants with undergraduate and graduate degrees were conscious in their food purchasing decisions.
• Among the participants, 49.50% of those with a household income of TRY 17,002 and below, 50% of those with a household income of between TRY 17,003 and 30,000, 53.60% of those with a household income of between TRY 30,001 and 50,000, 56.30% of those with a household income of between TRY 50,001 and 80,000, and 59.10% of those with an income of TRY 80,001 and above were found to be conscious in their food purchasing decisions. It is observed that an increase in household income level leads to a greater awareness in food purchasing decisions.
• It was determined that 57.60% of the participants in SES group A, 58.70% of the participants in SES group B, 47.20% of the participants in SES group C1, 50.80% of the participants in SES group C2, 47.70% of the participants in SES group D, and 41.50% of the participants in SES group E were conscious in their food purchasing decisions.

In the research, being conscious (1) is represented by 3.67 points and above, and (2) being unconscious is represented by 3.66 points and below. According to the logit analysis results of the research, it was revealed that gender, age, and educational status affect consciousness in food purchasing decisions at a 5% significance level.

• The logit analysis for the gender variable was interpreted as follows: Coefficient (B): −0.354. Since the coefficient is negative, the probability of occurrence of the dependent variable is lower for men (code = 1) than for women (code = 2). In other words, the “male” category of the independent variable has a decreasing effect on the dependent variable. Significance level (p): 0.043; p < 0.05. The gender variable is statistically significant, showing that gender has a significant effect on the dependent variable. Odds ratio: 0.702. Since the odds ratio is below 1, it is understood that being male decreases the probability of the dependent variable. Men are 29.8% less likely to realize the dependent variable compared to women (1 − 0.702 = 0.298). The analysis results show that women are 29.80% more conscious than men in their food purchasing decisions.
• The logit analysis result for the age variable is interpreted as follows: Coefficient (B): 0.161. Since the coefficient is positive, the probability of occurrence of the dependent variable is higher in the 18–29 age group (code = 1) than in the 60 and over age group (code = 5). In other words, the “age distribution” category of the independent variable has an increasing effect on the dependent variable. Significance level (p): 0.029; p < 0.05. The age distribution variable is statistically significant. This shows that age has a significant effect on the dependent variable. Odds ratio: 1.175. Since the odds ratio is above 1, it is understood that increasing the age group increases the probability of the realization of the dependent variable. Higher age groups are 17.7% more likely to realize the dependent variable compared to lower age groups (1.175 − 1.00 = 0.175). The analysis results show that individuals in higher age groups can be 17.70% more conscious in their food purchasing decisions than those in lower age groups.
• The logit analysis for the education level variable was interpreted as follows: Coefficient (B): 0.321. Since the coefficient is positive, the probability of the dependent variable occurring is higher for secondary school and below (code = 1) than for undergraduate/graduate education (code = 3). In other words, the “educational status” category of the independent variable has an increasing effect on the dependent variable. Significance level (p): 0.026; p < 0.05. The education status variable was found to be statistically significant. This shows that educational status has a significant effect on the dependent variable. Odds ratio: 1.378. Since the odds ratio is above 1, it is understood that an increase in educational attainment increases the probability of the realization of the dependent variable. Higher educational attainment is 37.8% more likely to realize the dependent variable than lower educational attainment (1.378 − 1.00 = 0.378). The analysis results show that individuals with higher educational status are 37.80% more conscious of their food purchasing decisions than those with lower educational status (

  Table 12. Logit analysis results of the identification of conscious consumers in food purchasing decisions.

  Variables	B (Coefficient of Explanatory Variables)	Standard Error	Z-Statistic	Importance Rating	Betting Odds
  Constant	−0.911	0.590	2.388	0.122	0.402
  Gender	−0.354	0.175	4.114	0.043	0.702
  Age	0.161	0.074	4.768	0.029	1.175
  Education status	0.321	0.144	4.979	0.026	1.378
  Household income level	0.060	0.079	0.584	0.445	1.062
  SES group	−0.37	0.076	0.240	0.624	0.963

  ).

The Nagelkerke R-square value expressing the explanatory power of the model is 0.038, the Cox and Snell R-square value is 0.029, and the likelihood value is 771.848.

5. Discussion

In this study, the general food purchasing tendencies of consumers are comprehensively revealed. To that end, food purchasing decisions regarding safe food, as well as the economic and environmental issues related to food, were analyzed. The statements were analyzed through many judgments, and the differences and similarities are shown in detail in the charts. It has been determined that food purchasing decisions are influenced by a multitude of factors, and that various situations, including concerns about food safety, environmental considerations, and economic factors, impact consumer decisions.

• Among consumers, checking the expiry date of the food and examining the packaging were the most important issues, while reviewing the nutritional values was the least important. It can be said that consumers care about the environment; they carefully create their shopping lists to minimize food waste, and they also prioritize conserving water. It can also be said that consumers mostly shop at close distances and from companies that care about them, and they also care about supporting local products.
• Women and men make different decisions based on many issues. It has been determined that women are more conscious than men on issues related to food safety such as reading food labels, buying satisfying foods, checking the additive content of food, being knowledgeable about the content of additives, reading the expiry date, checking the packaging, following expert opinions, and renewing shopping lists. It has been observed that women are more conscious than men on many issues related to the environmental impact of food, such as caring about recycling packaging, not wasting water, and consuming in season. Again, it is evident that women prioritize buying products with discounts and at affordable prices, as well as those with geographical indications or local products. It can be said that men, on the other hand, find government inspections more adequate, think that they can calculate nutritional values, and find large markets more affordable than women do. In general, we found that women are more conscious about environmental and economic issues than men.
• Especially among consumers under the age of 30, low label reading, other information studies, and the information provided in product content are considered sufficient. However, at older ages, it is noteworthy that expectations for understanding information beyond label reading, calculating nutritional values, and information studies increase. Additionally, it is understood that the environment becomes a more significant factor in food purchasing decisions after the age of 30, while economical considerations become more important when purchasing food between the ages of 30 and 60.
• It can be safely said that higher educational attainment significantly increases attention in food purchasing decisions, including food safety, environmental protection, and improvement in the economic aspects of food.
• The low level of household income may cause the importance of food safety and environmental protection to be pushed to the background in some cases. It is understood that high-income groups pay particular attention to environmental protection. As the household income increases up to TRY 80,001, it is determined that the attention to economic issues in food purchasing decisions also increases.
• SES groups A and B are more conscious about almost all aspects of food safety and environmental protection than other groups. It has been observed that SES groups A and B are more concerned with local products and branded products. It is noteworthy that all SES groups pay attention to not wasting food and water.

To improve nutrition and public health awareness, ensuring that individuals develop the right eating habits should be a priority issue in food information for both individual and public health. It is known that developing correct nutritional awareness in individuals from childhood onwards through continuous training reduces health expenditures, health-related public investments, and other economic and social burdens associated with malnutrition or undernutrition in individuals and society. It should be ensured that nutrition suitable for health in every age group is learned.

It was determined that consumers calculate nutritional values (2.78), purchase satisfying foods (3.60), check the additives on labels (3.63), and understand the harmful effects of additives (3.61).

In this study, in order to improve nutrition and public health awareness, the following is recommended: teach basic concepts related to food and nutrition, strengthen nutrition knowledge by learning food ingredients, improve nutrition behavior, learn what to do continuously for a healthy diet, and organize training to understand public health and its importance.

Awareness and understanding of reading food labels and interpreting information should be raised. Food labels are generally used to inform about nutrition and health claims and/or to find out the presence or amount of certain ingredients in the food. Consumers should be able to read food labeling information comfortably, understand whether the labeling is correctly presented on the packaging, and verify that the information about the food matches the information on the label. The labeling of foods is carried out according to certain principles adopted to harmonize with EU legislation and meet the requirements of labeling in international standards for world trade, drawing lessons from years of experience and taking into account developing technology and changing consumer needs. It is observed that the [44,45] of the Turkish Food Code (TGK) meet or approach the standards.

In this study, it was determined that consumers read labels (3.96), understand label information (3.70), understand nutritional values (3.56), find label information sufficient (3.04), think that label information reflects the truth (2.74), find label information sufficient (3.01), and want the visibility and comprehensibility of label information to increase (3.89).

Despite all these advances in labeling, this study found that consumers’ ability to read labels and understand the content is limited; they do not find label information sufficient, and most strikingly, they do not find label information reliable. In this regard, it can be concluded that consumers have a willingness to be educated, and based on this, it can be concluded that education should be provided.

Individuals and society should understand the importance of reading and understanding labels. Consumers’ needs regarding labeling should be well understood, their nutritional knowledge should be increased, and their habits of reading label information should be developed. Consumers are expected to understand labels and other information and make food purchasing decisions with awareness. For understanding food label reading/information and raising awareness, understanding the importance of reading the label and other information, understanding the content of the label and other information, and questioning the information written on food labels, awareness training should be given in relation to current studies.

In order to improve the environmental protection awareness of the consumer, it is expected that the substances used in food production processes and production methods should be limited in terms of their impact on the environment, companies should be directed to produce within standards that protect the environment, and environmental legislation and policies should be updated. The fact that the decisions of consumers have an impact on the environment means that the decisions of consumers should be directed toward the protection of the environment. Consumers also have the potential to positively impact food–environment interaction. Information and/or symbols used on food labels, such as “environmentally friendly,” should be arranged in a way that attracts the attention of consumers and allows them to understand the message. By increasing the responsibility of individuals towards the environment, the following aims should be pursued: ensuring that the environmental signs on food labels are easily understood, understanding the functioning of food production and the food system, understanding the times when food can be found, raising awareness about food problems, evaluating the functioning of the food system, and ensuring that traditional, cultural, and ecological dimensions are realized in food purchasing decisions. With the development of environmental awareness among consumers, environmentally friendly practices can be supported, and consumers can ensure supply security.

Regarding our study, we observed the following aspects in relation to the food purchasing decisions of consumers: the importance of the environment in food production (4.03); packaging not being harmful to the environment (3.96); the importance of the recycling sign on the packaging (3.86); the use of recyclable packaging (3.88); monitoring the production process (3.78); certifications for environmental protection (3.53); preference of certified products (3.69); willingness to pay extra for environmentally friendly products (3.67); finding organic or natural products environmentally friendly (3.89); separating packaging as glass, metal, plastic, paper, etc. (3.71); creating a shopping list to avoid food waste (4.09); consuming in season (3.95); taking care not to waste water (4.03); following environmental information (3.55); and caring about environmental protection studies (3.91). It is evident that consumers are concerned about the environment, carefully create their shopping lists to minimize food waste, and are mindful of conserving water. Consumers expect that the environmental protection activities of food companies should be better monitored (3.97), and studies should be carried out to better understand the certifications for environmental protection (4.02).

In this study, in order to improve the environmental awareness of the consumer, the following actions are recommended: explain the effects of food systems on the environment; explain the effects of urbanization on food systems; explain the effects of food consumption on the environment; explain environmental signs, certifications, and management systems in food; explain food purchasing decisions that protect the environment; and understand the importance of recycling.

In this study, consumers’ pre-shopping list preparation (3.65) and willingness to pay extra for organic or natural products (3.63) were identified as areas for improvement. Food shopping places were analyzed, and consumers’ preference for nearby markets (3.89), traveling to more distant markets for favorable prices (3.28), finding large markets more economical (3.32), and considering local smaller markets to be more important (3.29) were determined.

Improving consumers’ awareness of the economic aspects of food purchasing will contribute to both the individual’s and their household’s economic well-being and the national economy, particularly in terms of the economic benefits of food. Food accessibility for consumers can be strengthened by informing and improving their economic food purchasing decisions. Improving the food purchasing decisions of individuals by considering their own economic situation requires raising their awareness and directing them to economic, local, and geographically relevant products. Making conscious decisions for the benefit of society can be increased by considering the sustainability of food systems, emphasizing issues such as reducing food waste. Improving the economic purchasing decisions of individuals can ensure that they buy the right product at the right time and from the most appropriate place. Individuals can evaluate the messages they receive from food information sources and the advertising and promotional activities of companies in accordance with their budgets. In this regard, food access can be increased by guiding the economic preferences of individuals, and food systems can be transformed into a more ethical and responsible structure.

In this study, the statements of consumers regarding the importance of domestic food production (3.88), support for local products (4.03), willingness to use geographically marked products (3.72), and willingness to pay extra for geographically marked or local products (3.57) were assessed.

Regarding our study, consumers’ expressions in relation to a number of areas were determined: affordable food products (3.78), willingness to pay extra for branded products (3.13), following campaigns (3.51), willingness to buy according to campaigns (3.43), preference for discounted products (3.63), the effect of advertising (3.10), company trust (3.81), and the attitude toward companies that care about the consumer (4.08). It is expected that more interventions regarding food prices will be necessary to protect the consumer (3.98), as well as to increase studies that protect consumers from sudden increases in food prices (4.08).

According to our study, in order to improve the awareness of economical food purchasing, it is suggested that consumers should be educated in the following areas: food economics and its effects; explaining the economic issues of food waste, nutrition, and food utilization; explaining the places and times of economic food purchasing; gaining economic literacy of food; and explaining the economic effects of purchased food and raising awareness.

In this study, factor analysis was conducted to identify the factors influencing food purchasing decisions. The factor analysis explained food purchasing decisions at a level of 58.90%, with a Cronbach’s alpha value of 0.960 (highly reliable). After conducting factor analysis on food purchasing decisions, the following subgroups were identified: environmentalism, economy, conservatism, attentiveness, informativeness, caring, and transformativeness. Thus, the factors that consumers pay attention to in food purchasing decisions were determined using factor analysis. In the findings of this study, the characteristics of each factor and their effect regarding food purchasing decisions are given in detail; this aims to be a valuable resource for other studies regarding the food purchasing decisions of consumers.

After the factor analyses, the differences in the factors considered in food purchasing decisions were analyzed using Mann–Whitney U tests for gender and Kruskal–Wallis tests for age distribution, education level, household income level, and SES group. As a result of the analyses, it was determined that demographic characteristics and SES groups created significant differences in most factors related to food purchasing.

• According to the research, women pay significantly more attention to the environmentalism, caring, and innovation factors than men.
• While those under 40 pay less attention to the advanced innovation factor, those aged 40 and over pay more attention to it. Those who pay the most attention to the innovation factor are those aged 60 and over.
• It has been determined that those with a high school diploma and above pay more attention to the conservatism factor than those with a diploma below the high school level. On the other hand, it was determined that bachelor’s and postgraduate graduates pay more attention to the informativeness factor compared to others.
• Those with a household income level of TRY 30,000 and above pay more attention to the conservatism factor. Those who pay the most attention to the conservatism factor are individuals who have a household income level of between TRY 50,001 and 80,000.
• It is understood that higher SES groups, such as A, B, and C1, pay more attention to the conservatism factor compared to the C2, D, and E SES groups. Those in the B SES group were found to pay the most attention to the conservatism factor. As for the informativeness factor, it has been found that those in SES groups B and A, followed by those in SES group C2, pay more attention to this factor than those in SES groups C1, D, and E.

In this study, it was determined that demographic characteristics and SES group create differences in food purchasing decisions, and it was emphasized that different educational studies should be carried out in accordance with their characteristics. This study aims to contribute to other research by explaining the differences and similarities identified in the aforementioned issues, as presented in the findings.

• In this study, the consciousness of consumers in food purchasing decisions was analyzed. A total of 51% of the participants were found to be conscious. A total of 54.20% of women and 46.80% of men were found to be conscious. Among those with secondary school education or less, 40.10%, 52.10%, 56.30%, and 56.30% of those with high school and undergraduate or undergraduate and graduate degrees, respectively, were found to be conscious. It was found that 57.60% of those in SES group A, 58.70% of those in SES group B, 47.20% of those in SES group C1, 50.80% of those in SES group C2, 47.70% of those in SES group D, and 41.50% of those in SES group E were conscious in their food purchasing decisions. Additionally, it was found that attention to food purchasing decisions increased with higher household income levels.
• In this study, logit analysis was also applied to identify the profiles of conscious consumers in their food purchasing decisions. It was found that gender, age, and educational attainment were significantly associated with consciousness in food purchasing decisions. Men are 29.8% less likely to be conscious compared to women. Higher age groups are 17.7% more likely to be conscious than lower age groups. Those with higher education are 37.8% more likely to be conscious than those with lower education.

6. Conclusions

Although the demand for food is steadily increasing today, it is known that a large number of people still cannot access adequate and balanced nutrition, that food systems have negative impacts on the environment, and that food needs to be transformed, especially in response to problems such as climate change [46,47,48]. Sustainable food and environmental management policies should be included among the strategic objectives in ensuring food security [49].

Consumers may not believe that sustainability influences their food choices. It may be useful for decision-makers to communicate sustainability information about the environmental impact of food to consumers in a transparent, evidence-based, and controlled way [50]. Consumers should be involved in food policies and should be informed about foods and the impacts according to their age, education level, and other characteristics, as seen in this study. If consumers are unaware of the impacts of their food purchasing decisions, the development of sustainable food systems may not be successful.

Sustainability in food policies can be achieved through the following aspects: raising awareness of the environmental [51,52] and economic issues related to food, ensuring fair distribution by recognizing food security as a right for everyone, transforming food systems through inclusive approaches that involve all elements of the system, building a resilient and stable food structure, and promoting the ethical and responsible consumption of food.

Responsible and ethical food consumption, which is also required by governments, should aim to promote the understanding of sustainable food consumption by explaining ethical, responsible, and conscious consumption issues and the environmental, economic, and health impacts of consumer decisions in food purchasing on food systems. Sustainable food consumption aims to promote diets that are both sustainable and nutritious, preserving individual health while protecting environmental, economic, and social values for future generations. It also contributes to the sustainability of food policies by guiding ethical and responsible food purchasing decisions.

This study holds inspiring significance not only for the province of Istanbul but also for global food consumption efforts. The research presents a scalable framework designed to enhance consumer awareness, promote ethical consumption, and encourage responsible food choices across diverse socio-economic contexts. The findings obtained from Istanbul may serve as a valuable guide for other global metropolises facing similar challenges. Ultimately, this study contributes to the global transformation of food systems towards a more just, ethical, and sustainable structure, while also presenting a vision aligned with the United Nations Sustainable Development Goals.

In terms of sustainable food policies, this study revealed that further work is needed to enhance consumers’ food purchasing decisions, increase their awareness of ethical and responsible consumption, inform them, and encourage their participation and active engagement in the food system. Therefore, the demands and needs of consumers should not be ignored. By ensuring that producers adopt sustainable and ethical production methods while taking consumers into account, both a healthy and safe food supply will be ensured, and long-term food safety and sustainability goals will be achieved.

It can be seen that the results of this research support studies that are grounded in the conceptual framework outlined in the introduction and summarized in the literature review. Additionally, the consistency with the research objectives is evident when examining the findings and discussion sections. Based on the province of Istanbul, the research enables us to examine the effects of urbanization on responsible and ethical food consumption, as well as the importance of various consumer characteristics in relation to food policies [53].

The research is limited to a sustainable food consumption survey conducted with consumers in Istanbul and a literature review. This study aims to contribute to the literature and shed light on the topic for policymakers, especially in other rapidly urbanizing provinces.