Assessment the Adoption Level and Factors Influencing the Adoption of Circular Economy Among the Adult Consumers in the Eastern Province of Saudi Arabia

1. Introduction

Recently, the circular economy (CE) has emerged as a critical topic, garnering heightened attention due to its substantial solutions for mitigating food waste issues. [1] defined circular economy (CE) as a regenerative system that minimizes resource input and waste, emissions, and energy leakage by slowing, closing, and narrowing material and energy loops. This can be accomplished by durable design, maintenance, repair, reuse, remanufacturing, refurbishing, and recycling. Consequently, the circular economy (CE) is a process designed to maintain value via maintenance, reuse, refurbishment, remanufacturing, recycling, and composting [2]. [3] contended that the circular economy (CE) constitutes an evolving framework focused on the recirculation of energy and resources, reducing resource demand, reclaiming value from waste through reuse, reduction, or recycling, and implementing a multi-tiered strategy to attain sustainable development through a strong connection with societal innovation. Furthermore, the Circular Economy (CE) is predominantly depicted as an alternative framework that provides both economic and environmental benefits. as analyzed by [1]. The circular economy (CE) has experienced a paradigm shift, currently prioritizing sustainable development at three levels: micro (businesses and customers), meso (economic actors involved in industrial symbiosis), and macro (cities, regions, and governments) [4].

Redesigning items and materials for circular use would also inspire new ideas in many areas and levels of the economy. [5] observed the lack of comprehensive strategies for the implementation of the circular economy (CE) at all organizational levels. They stressed the need for models that deal with problems on all three levels: micro, meso, and macro. As a result, a universally recognized approach for measuring the circular economy at the micro level, as well as for evaluating its specific principles like recycling and remanufacturing, is absent [3]. The significance of recycling resides in its capacity to alleviate supply-related problems, including price volatility, material scarcity, and reliance on imports.

Consumers play a crucial role in the circular economy (CE) loop [6,7,8]. They put circular economy ideas into action by making choices and acting in ways that extend the life of products, reduce waste, and use fewer resources. This is how they manage consumers from a circular economy point of view [9]. The CE helps consumers by making products that last longer and are more useful, which improves their quality of life and saves them money in the long run. But adopting the circular economy is not easy. Recent research has underscored several critical factors that affect consumer behavior in this context: economic considerations, the congruence of consumer demands with market offerings, information accessibility, social influences, and personal preferences and beliefs. Consumer decision-making is a fundamental process that influences behavior [10]. Consequently, it is imperative to identify the variables that motivate consumers to adopt more circular habits, especially when such changes may require significant lifestyle adjustments [8]. Therefore, understanding how consumers make decisions is important because it affects how long and how often they use goods that are meant to be used for a long time; this outcome ultimately depends on the users themselves [11,12]. Therefore, understanding customer engagement in circular processes is crucial, as consumer empowerment is integral to the circular economy framework [13]. [14] recently stressed the need for more empirical research on how communities and consumers are involved in circular activities.

Presently, new methodologies are propelling the circular economy in the food sector, encompassing novel company structures and digital marketplace platforms for food sharing. These platforms, based on a circular model, link food producers and vendors with reuse organizations, charities, and consumers, therefore markedly diminishing food waste [15]. A circular economy approach to food waste acknowledges and maintains the value and usability of food products, nutrients, and resources for an extended duration; this reduces resource consumption while promoting the upcycling of food waste and by-products. Thus, the CE framework is essential for minimizing food waste and enhancing overall sustainability.

A major reason why food goes to waste is that home food distribution is not managed well. Making plans for getting and storing food is not easy. A common behavior is to buy too much food without thinking about how it would spoil [16]. Even if customers try to follow producers' advice, some food goes bad because the date labels on the products are unclear. Also, there are other problems in the home that make it hard to manage the pantry well, like family members having different tastes in food and unexpected social events. CE models are a good way to reduce food waste, which is currently at a high level [17,18]. Additionally, the deployment of consumer-engaging CE systems would capitalize on the shifting consumer perceptions on sustainable living [19]. Nonetheless, tackling the consumer aspect of the circular economy remains difficult [20]. People also need to become used to new technology that is meant to recycle biological materials and close the food-waste loop. The circular economy offers new ways and technologies for recycling materials, but these technical solutions won't work unless people adopt them. To deal with food waste at the consumer level, consumers need to be actively involved in making decisions about how to buy and throw away food [20].

Saudi Arabia doesn't have a lot of land and water to grow food, which means that a lot of food goes to waste, using up these limited resources on food that isn't eaten. Because of this, the country relies a lot on big imports and food subsidies to meet its food needs [21]. Food waste is a major risk to the safety of the food supply in the country [21,22]. [23] identify the primary factors contributing to food waste in Saudi Arabia as insufficient social awareness, the conduct of dinner hosts, and a propensity to display extravagant cuisine to guests. A strong economy, cultural traditions that link celebrations to food waste, and a lack of knowledge about the effects of food waste are the main national factors that cause waste.

[22] discovered that food waste is an issue due to insufficient laws, policies, or distinct collection systems for various types of solid waste. They also learned that people don't like food waste, don't know much about it, and don't want to use food waste byproducts. They also learned that pre-treatment before landfilling is not a common way to get rid of waste. This is why the CE is an important way to cut down on food waste in Saudi Arabia. The government still doesn't have a clear plan for a circular economy, which makes it harder for it to deal with important issues like waste management and over-consumption [24]. [25] conducted research that highlighted the potential implementation of circular economy principles to enhance sustainability across supply chains.

The mentioned literature revealed that many studies have recognized food waste as a critical concern regarding food security and sustainability; however, there is a lack of research on the implementation of the circular economy (CE) as an essential strategy to reduce food waste in Saudi Arabia's food sector. The principal shortcoming in research regarding consumer adoption of the circular economy in Saudi Arabia is the absence of studies assessing the extent of circular economy adoption among consumers and the impact of socioeconomic characteristics and consumer behavior on the actual implementation of circular economy practices among diverse adult consumers in Saudi Arabia. Therefore, this research aims to fill the gap by (1) evaluating the extent of consumer adoption of the circular economy as a method for minimizing food waste and (2) identify the elements that affect this adoption, encompassing socioeconomic characteristics, consumer behaviors, and attitudes towards the circular economy. The study also seeks to furnish policymakers with actionable strategies to reduce food waste and increase consumer engagement in circular economy initiatives, thereby improving food security and attaining food sustainability.

2. Materials and Methods

2.1. Sampling Size

The sample was taken from the Eastern Province between September and August 2025. The Eastern Province has a lot of different businesses, such as farming, trade, and making things. Indigenous traditions and globalization have both had a direct effect on the attitudes and behaviors of people in the Eastern Province. Participants were recruited on social media using a convenience sampling method and the sample size was estimated using the Stephen Thompson technique [26]. The Stephen Thompson equation is expressed as follows:

$$\mathbf{n}={\displaystyle \frac{\mathbf{N}\times \mathbf{p}(1-\mathbf{p})}{\left[\left[\mathbf{N}-1\times \left(\frac{{\mathbf{d}}^2}{{\mathbf{z}}^2}\right)\right]+\mathbf{p}\left(1-\mathbf{p}\right)\right]}}$$

(1)

Whereas:

N: the size of the study community

Z: the normative degree corresponding to the level of significance is 0.95 and is equal to 1.96

d: the error ratio is equal to 0.05

p: the ratio of availability of property and neutral is equal to 0.50

$$\mathbf{n}={\displaystyle \frac{\mathbf{2,949,854}\times \mathbf{0.50}(\mathbf{1}-\mathbf{0.50})}{\left[\left[\mathbf{2,949,854}-\mathbf{1}\times \left(\frac{{\left(\mathbf{0.05}\right)}^{\mathbf{2}}}{{\left(\mathbf{1.96}\right)}^{\mathbf{2}}}\right)\right]+\mathbf{0.50}\left(\mathbf{1}-\mathbf{0.50}\right)\right]}}$$

The total number of populations is about 2,949,854 individuals according to the [27]. Thus, the study sample consists of 384 individuals [28].

2.2. Data Collection

A cross-sectional study was carried out using primary data with the help of a structured questionnaire to determine the level of adoption of circular economy (CE) and to identify the factors influencing the adoption of CE among adult consumers of the Eastern Province of Saudi Arabia. There were four key parts to the questionnaire. The first part is about things like sex, age, marital status, education level, occupation, and monthly income. The second part had information about how people buy food, like how they shop for it, how much they spend on it, and why they buy it. The third section included the behaviour of individuals towards the CE practices, and how individuals rationalize their food consumption. In this section, the participants were asked about their practices regarding the recycling of leftover food, their familiarity with the term "Circular Economy," their home recycling habits, the use of applications for food recycling, and the motivations behind rationalizing food consumption within the circular economy framework. There were five reasons for utilizing the CE system to rationalize food consumption, and then the participants were asked to rate their motivation for doing so on a five-point Likert scale (strongly disagree, disagree, neutral, agree, highly agree). The information on how the CE system helped people rationalize their dietary choices included in section four. The participants were encouraged to think about how they may use CE practices to make their food choices more reasonable. It featured the recycling of important items including bread, dates, cooked rice, cooked pasta, stewed vegetables, stewed meat, and fruit. We chose the graded scale (Yes / Sometimes / No) because it is simple to grasp and apply, and it gives people more freedom to appropriately express their ideas. The "sometimes" option helps to decrease bias and deliver more detailed data by showing the difference in views instead of making participants give a clear answer. This scale also makes it easier to analyze data without the complications that more detailed scales can bring. This makes it a good choice for gathering clear and easy-to-understand data, as [29] point out.

Before being sent out, the questionnaire was checked by a group of experts to make sure it was well-organized and met the study's goals. The questionnaire has certain requirements that must be met for people to be in the right demographic category. To be eligible, Saudi citizens must be at least 18 years old and live in the Eastern Province. Pilot research was executed by administering the questionnaire to 25 people who met the eligibility criteria for the survey. Their remarks were used to figure out what problems the participants had while filling out the questionnaire. Changes were made based on what people said. The primary data was gathered via distributing the questionnaire electronically through social media [28]. The study also used secondary data from relevant sources.

2.3. Data Analysis

2.3.1. Assess the Extent of Circular Economy Implementation

To assess the level of adoption, participants were asked how they justified their dietary choices in respect to the CE system and their own beliefs, using a graded scale (yes/sometimes/no) as examined by [29]. We asked the participants about the way of recycling seven food items: bread, dates, cooked rice, cooked pasta, stewed vegetables, stewed meat, and fruit. The participants' responses about food recycling might be expressed as follows:

The first step is to get three responses (yes, sometimes, or no) on a scale of 1 to 3. For example, "I throw it away in the trash" is one of seven questions like this.

Secondly, positive phrases came from three answers: "yes," "sometimes," and "no," on a scale of 3 to 1, such as: sorted and ground to make different recipes, used to make other dishes, turned stale bread into breadcrumbs (rusks), and used expired products as feed for animals. The total number of questions given above is 40.

Thirdly, the greatest degree of this aim is 120 degrees (40*3), and the minimum degree is 40 degrees (40*1).

Fourthly, the levels of the circular economy are classified as follows: a low level is less than 50% of the maximum degree (below 60 degrees), mid-level is 50% to less than 70% of the maximum degree (from 60 degrees to below 72 degrees), and a high level is more than 70% of the maximum degree (above 72 degrees and beyond).

Before identifying the CE level, the reliability test of the 40 questions that were used to construct the CE level was applied using Cronbach's alpha. The reliability coefficient of Cronbach's alpha was equal to 0.931, indicated the satisfactory internal consistency among all questions (See Appendix No. 1)

2.3.2. Ordered Logistic Regression Model (OLRM)

Ordered logistic regression is a statistical method for modeling an ordinal dependent variable with categorical outcomes (such low, medium, and high) that have a natural order but no clear lines between categories. The model demonstrates the influence of predictors on the likelihood of classification into higher or lower result categories, based on the proportional odds assumption. The proportional odds logistic regression model analyzed the ordinal dependent variables [30,31].

The model posits the presence of a continuous latent variable y* that regulates the observed ordinal categories y, as delineated by [32].

y∗=βiXi+ε

(2)

where Xi represents predictors, βi denotes coefficients, and ε follows a conventional logistic distribution as the standard error. The observable Y is defined by thresholds (cut point) μj

$$Y \left\{\begin{array}{c}\mathbf{1} \mathit{i}\mathit{f} {\mathit{y}}^{\mathit{*}}\le {\mathit{\mu }}_{\mathbf{1}}\\ \mathbf{2} \mathit{i}\mathit{f} {\mathit{\mu }}_{\mathbf{1}}<{\mathit{y}}^{\mathit{*}}\le {\mathit{\mu }}_{\mathbf{2}}\\ .\\ .\\ .\\ \mathit{J} \mathit{i}\mathit{f} {\mathit{y}}^{\mathit{*}}>{\mathit{\mu }}_{\mathit{J}-\mathbf{1}}\end{array}\right.$$

(3)

For J ordered categories, the model estimates cumulative logits:

$$\mathit{log}\left({\displaystyle \genfrac{}{}{0pt}{}{\begin{array}{c}\mathit{P}(\mathit{Y}\le \mathit{j})\\ \_\_\_\_\_\_\_\_\end{array}}{\mathit{P}\left(\mathit{Y}\mathit{˃}\mathit{j}\right)}}\right)={\alpha }_j+{\beta }_j{X}_j \mathit{for} j=1, 2,\dots ,j-1$$

(4)

αj = μj are category cutpoints (intercepts, one fewer than categories).

β are shared regression coefficients (one per predictor).

The formulas show that the ordered logistic model can use the estimated value of the latent variable and the assumed logistic distribution of the disturbance term to figure out the chance that the unobserved variable Y* falls within certain threshold limits [33]. When using ordinal data, ordered logistic regression estimation uses maximum likelihood estimation (MLE) to estimate both the regression coefficients (β) and the category cutpoints α_j at the same time.

In our research, the dependent variable (y) represents the extent of CE adoption, classified into three categories: low level, medium level, and high level. The low level is less than 50% of the maximum degree (less than 60 degrees), the mid level is between 50% and less than 70% of the maximum degree (from 60 degrees to less than 72 degrees), and the high level is more than 70% of the maximum degree (greater than 72 degrees). The explanatory elements include socioeconomic characteristics, consumer habits, and attitudes toward the circular economy (CE). In this way, the model specification can be stated as follows:

Logit [Pr(Y≤j|X)] =αⱼ+β₁X₁+β₂X₂+β3X3+β4X4+β5X5+β6X6+β7X7+β8X8+β9X9+εi ………

(5)

whereas:

Y: the of CE adoption (1= low level, 2= mid-level, 3= higher level)

αⱼ: cutpoints

X1, X2,……, X3 : the explanatory variables

β₁, β2, ..., Β9: the shared regression coefficients

εi: standard error follows a standard logistic distribution

Table 1 delineates the explanatory variables utilized in the ordered logistic regression model (OLRM), along with their corresponding codes and symbols employed in the model. Nine independent variables were employed to evaluate the extent of CE adoption. The nine predictors were selected because they were the most theoretically and empirically relevant variables available for the model. This limited set was chosen to maintain model parsimony, avoid unnecessary complexity, and improve the stability and interpretability of the results.

3. Results and Discussion

3.1. Socioeconomic Characteristics and Consumer Behaviours, Attitudes Toward CE

Table 2 shows the results of the descriptive data about socioeconomic variables, consumer behaviors, and opinions about CE in the Eastern Province of Saudi Arabia. The statistics show that 59.9% of the people who took part are male and 40.1% are female. There may be big differences between male and female when it comes to adopting circular economy techniques. For example, female are often the ones who start home projects like reducing trash and making the most of food. The research conducted by [34] highlighted structural inequalities, illustrating how the circular economy reinforces the gender gaps inherent in the linear economy, particularly with women being disproportionately involved in informal recycling. Research conducted by [35] indicated that females demonstrate heightened awareness of business environmental practices compared to males, as expected. About 34.6% of the people who took part were between the ages of 25 and 35, and 39.1% were between the ages of 35 and 45. Only 3.6% of the people who took part were between the ages of 55 and 65. This means that most of the people who took part are young enough, which indicates that there is a lot of opportunity for them to start doing things like recycling resources and reducing food waste.

The average family size has about five members, which could affect how much food is wasted and how much is recycled. According to [36], the size of a household impacts how much waste it makes, how it is managed, how it is sorted, and how it is reused. This increases the potential for circularity. [37] found that families with more members are more likely to use circular economy approaches. On the other hand, roughly 29.9% and 32% of the people who took part said their monthly income was (> 2000 to 4000 SR) and (> 4000 to 6000 SR), respectively. People who make more money each month are more likely to follow circular economy practices because they can invest in new technologies and innovations that make better use of resources. Because of money problems, lower-income families often do circular economy activities that are necessary, including fixing things or reusing them. They have trouble adopting more advanced methods [34]. The findings show that 59.6% of the people who took part are employees and 21.9% are freelancers. About 6.5% of the members are out of work, and 0.8% are retired.

The marital status indicates that 70.6% of participants are married, while 29.4% are single or divorced. The marital status indirectly affects CE adoption through home dynamics, shared responsibilities, and resource pooling, however direct studies are scarce, as noted by [37]. Recent study indicates that married individuals or couples frequently exhibit greater engagement in circular economy practices due to collaborative decision-making and familial demands for sustainability, including collective trash reduction and second-hand purchases influenced by partners, particularly women who guide household behaviors. Single-person families may emphasize personal cost-saving reuse while implementing less systematic innovations [38]. The results indicate that 45.3% of participants reported consistently recycling their food stuff, whereas 47.4% indicated that they recycle their foodstuffs occasionally. Conversely, 27.6% indicated that they are utilizing applications and platforms for the circular economy, while 51.8% stated that they use such applications and platforms "sometimes." So, mobile apps that help people recycle directly support the concepts of a circular economy by making it easier to sort, collect, and recover materials for reuse. On the other hand, 97.7% of the people who took part disagree that the unwillingness to keep leftover food is a cause main reason for food waste. The unwillingness to keep leftover food is a major reason why people waste food. This is often because they think it's too hard, they don't like the taste, or they don't store it properly. This plainly goes against the principles of a circular economy by sending resources that may be reused to landfills instead of being reused. This lowers their economic worth and raises emissions.

Approximately 96.1% of participants do not cite boredom as a motivator for purchasing food. Consumer motivations for purchasing food, including convenience, cost, quality, and health, strongly correlate with circular economy principles by affecting waste production, resource cycles, and sustainable sourcing. Consequently, when experiencing boredom, consumers pursue immediate sensory pleasure (e.g., consuming packaged snacks), circumventing intended purchases and augmenting leftovers by 15-25% as a result of overconsumption [39] (Ostrowska, 2025). The average rationalization motivations utilizing the CE system are determined based on five criteria evaluated through a five-point Likert scale (strongly disagree, disagree, neutral, agree, strongly agree). The variables were employed to evaluate the motivational direction of individuals in rationalizing food consumption through the CE system, including strategies such as reducing expenditures to navigate challenging economic circumstances like debt, pre-planning meals, and avoiding impulsive purchases to mitigate excess food quantities, the intention to lessen environmental detriment by minimizing food waste, and effectively utilizing resources while ensuring their sustainability. The average value of rationalization reasons utilizing the CE method is 1.6, signifying a "Disagree" response, which reflects a low level of agreement or endorsement of these motives by respondents. This indicates that individuals predominantly do not justify their conduct using CE reasons.

3.2. Adoption Level of the Circular Economy

The circular economy classification seeks to analyse how individuals justify their food consumption to address food waste. This also categorizes individual behavior about circular activities. Table 3 presents the classification of adult customers based on their level of circular economy adoption in the Eastern Province of Saudi Arabia. The results indicate that 3.1% of individuals exhibit a low level of CE, whereas 34.4% have a medium level of CE. Approximately 62.5% of the participants exhibit a high level of consumer engagement. Results demonstrate low desire for rationalization of the CE system, but very high CE adoption rate (62.5%), which may imply that the adoption may be driven by external or practical rather than motivational support. The comparatively high adoption rate may reflect the influence of external facilitators such as governmental pressure, stakeholder expectations, market demand, technological availability and financial incentives which can facilitate CE implementation even when rationalization motivation is low. [40] contended that the CE categories facilitate the identification of how consumers rationalize food consumption to mitigate waste by dividing actions into linear versus circular stages, hence elucidating shifts in thinking from disposal to value retention. A study conducted in Vietnam by [37] categorized the levels of circular economy (CE) adoption into four tiers: “no intention to adopt CE,” “intention to adopt CE but uncertain about the timeline,” “currently in the process of adopting CE,” and “CE has already been adopted.” A study conducted in Menoufia Governorate by [29] revealed that 80.6% of families exhibited low to moderate levels of consumption reduction through the circular economy.

3.3. Factors Affecting the Circular Economy Level

Table 4 presents the results of the ordered logistic regression model (OLRM). The model fit results indicate that the likelihood-ratio χ² is 75.076 with 18 degrees of freedom (df). This result is highly significant; the P < 0.01 indicates that, assuming the null hypothesis asserting that all effects are equal to zero is valid. This indicates that the independent variables exert a statistically significant influence on the level of CE. Numerous pseudo-R² statistics have been suggested for the ordered logistic model. McFadden's R² is among the most renowned. The pseudo R² varies from 0 to 1; a higher number indicates a stronger link. The value of pseudo R² in this model is 0.127. This indicates a 12.7% enhancement in the anticipated result based on the predictors relative to the null model.

The cutpoints are evaluated for their utility in estimating the CE level. The estimated values of -1.789 and 1.757 indicate the predicted values for the observed Y:

Y_i=1 if Y*_i is≤ -1.789

Y_i=2 if -1.789≤Y*_i ≤ 1.757

Y_i=3 if Y*_i ≥1.757

The sign of the estimated value shows how likely it is that a case belongs to a certain category of the dependent variable. This is how the explanatory variables are understood. Table 4 shows the socioeconomic factors, consumer behaviors, and attitudes toward circular economy (CE) that affect the adoption of CE in the Eastern Province of Saudi Arabia. The results show that a larger family size is linked to a higher chance of reaching a higher level in the dependent variable (i.e., a higher CE level). So, as the number of individuals in the family of the respondents grows, the chances of reaching a high CE level also grow. This shows a positive and significant relationship at the 5% level. When there are a lot of individuals in a family, the chance of a high CE level is 1.21 times higher than when there are only a few individuals in the family. [41] found that in larger households, cost often outweighs the desire for environmentally friendly goods, making it harder for green shopping patterns to become widespread.

Those who received "≤ 2000 SR" a month are more likely to reach a high CE level than those who earned "˃ 10000 SR," with a significant difference at the 1% level. The likelihood of attaining a high CE level is 7.76 times larger for persons who achieved “≤ 2000SR.” Participants earning "˃ 2000 - 4000 SR" monthly have an increased likelihood of achieving a high CE level in comparison to the reference group, with the disparity being statistically significant at the 1% level. When participants had between "2000 and 4000 SR", their chances of having a high CE level were 9.80 times higher. Adults who earned "˃ 4000 - 6000 SR" a month are much more likely to reach a high CE level than the reference group. This is true at a level of 1%. When individuals had "˃ 4000-6000 SR," the chance of a high CE level was 8.13 times higher. On the other hand, people who received "˃ 6000 - 8000 SR" monthly are more likely to reach a high CE level than the reference group. This difference is statistically significant at the 1% level. When people had "˃ 6000-8000 SR," the chance of a high CE level was 7.0 times higher. Moreover, individuals earning "˃ 8000-10000 SR" monthly exhibit an increased probability of achieving a high CE level relative to the reference group, with a statistically significant difference at the 10% level. When people have between " ˃ 8000 and 10000 SR", the chances of having a high CE level are 4.22 times higher. Recent research by [41] demonstrated that lower-income households often struggle to adopt sustainable purchasing patterns due to the occasionally high costs of commodities like organic food and fruit. Moreover, [37] provided significant evidence of the positive impact of circular economy adoption on household income in the Red River Delta, Vietnam. [29]conducted a study that demonstrated a positive and significant impact of monthly income on the rationalization of consumption via the circular economy in Menoufia Governorate.

The participants who were called "students" were more likely to be in the low CE level than the reference group (i.e. retired), with a statistically significant difference at the 5% level. The people who were called "employees" are more likely to be in the low CE level than the reference group, with a significant difference at the 10% level. The participants who are called "freelancers" are more likely than the reference group to reach a low CE level, and the difference is statistically significant at the 5% level. Participants who are "unemployed" are more likely to fall into the low CE level than the reference group, and the difference is statistically significant at the 5% level. The chance of a high CE level going down for participants who are students, employees, freelancers, or unemployed, compared to the reference group, is the same. Because of this, the chances of having high CE levels go up when individuals retired. [41] study indicated that people's jobs had a big effect on their preferences for eco-friendly and green products. On the other hand, a study by [29] in Menoufia Governorate found that the jobs of male-headed households had a big effect on how people used the circular economy to make their consumption more rational. On the other hand, the jobs of female-headed households had a negative effect on how people used the same framework to make their consumption more rational.

The effect of marital status shows that people who are "unmarried" are more likely to fall into the low CE level than those who are married. However, this difference is not statistically significant. The results show that married individuals are more likely to have higher CE levels. [41] found that marital status affects green shopping behavior. Married individuals are more likely to buy eco-friendly products than single individuals, possibly because they care more about the long-term health of their homes and the environment. [35] argued that married persons demonstrate heightened awareness of corporate social responsibility practices.

Individuals who do not recycle food stuff demonstrate an increased probability of exhibiting greater CE levels in comparison to the reference group (i.e., those who recycle their food items); nonetheless, the difference lacks statistical significance. As a result, people who don't recycle their food have a 1.19 times higher chance of having a high CE level than people in the reference group (i.e., people who do recycle their food). On the other hand, people who sometimes recycle their meals are more likely to have a low CE level than the reference group, and this difference is statistically significant at the 10% level. When participants sometimes recycle their food, the chance of a high CE level goes down by 0.66 times compared to the reference group.

The effect of using applications and platforms for the CE shows that people who didn't use these tools are more likely to get a low CE level than people who did use these tools. As a result, the findings show a big difference at the 1% level. When participants don't use applications and platforms for the CE, the chance of getting a high CE level is 0.37 times lower. On the other hand, people who used applications and platforms for the CE sometimes are more likely to have a lower CE level than the reference group. As a result, the chances of people getting more involved in the circular economy go up as more individuals use applications and platforms made for this purpose. This means that the odds ratio of significant circular economy participation will go down if people don't use apps and platforms that are related to the circular economy. The study conducted by [37] determined that technology is the essential tool for achieving a circular economy, whereas insufficient technology represents a barrier preventing households from adopting the circular economy.

Participants who disagreed that unwillingness to store leftover food is a cause of food waste are more likely to be in the high CE level than those who agree that unwillingness to store leftover food is a cause of food waste. This difference is important at the 1% level. This indicates that those who disagreed that food waste results from unwillingness to keep leftovers possess a 7.89 times greater probability of exhibiting a high CE level compared to the reference group.

On the other hand, individuals who do not perceive boredom as a motivating element for food shopping are more likely to attain a high CE level compared to the reference group (i.e., where boredom is considered a motivating factor for food purchasing). So, the difference is statistically significant at the 1% level. When individuals are not driven by boredom to get sustenance, the probability of a high CE level increases by 4.94 times.

The decrease in the average rationalization motivations utilizing the CE system suggests a higher probability of classification within the lower spectrum of dependent variables (i.e., low CE level). As a result, as the usual reasons for rationalizing the CE system go down, the chances of having a low level of CE go up. This shows a strong and negative relationship at the 10% level. Respondents with low average rationalization reasons concerning CE are 0.74 times less likely to have a low CE level.

The parallel lines test checks the assumption of the ordinal logistic regression model. This means that the effect of the predictors stays the same across all levels of the dependent variables. So, the chance that the predictor will match the response categories stays the same. The chi-square value is 7.017, and its lack of significance means that the chance of moving up to a higher category stays the same across all of the dependent variables.

4. Conclusions

This study aims to evaluate the extent of CE adoption among adult consumers in the Eastern province. A cross-sectional design was used to collect primary data via a structured questionnaire from a sample of 384 participants aged 18 years and older. The results indicate that despite a greater number of participants adopting a high level of circular economy (CE), certain socioeconomic traits, consumer behaviors, and attitudes about CE still influence the level of CE engagement. These factors may contribute to the prevalence of elevated food waste in Saudi Arabia. Consequently, the implementation of the circular economy is essential to tackle the issue of food waste and improve food sustainability. The findings indicate that the study proposes several ways to enhance the level of CE adoption. The enhancement of adult consumers' incentive for rationalization through the circular economy (CE) and the promotion of food waste recycling via the provision of specialized technology and innovations that facilitate the implementation of circularity. Therefore, supporting the application and platform specialist in food recycling will also enhance the adoption of the circular economy. Nonetheless, it is essential to implement education and awareness initiatives concerning the evolving consumer behavior and attitudes towards food recycling to facilitate customer participation in a circular economy seamlessly. The government should promote food recycling at home and in restaurants by offering financial incentives to individuals who engage in food recycling to mitigate food waste. The government should implement legislation and regulations to mitigate food waste, which would significantly reduce the environmental, economic, and public health detriments associated with wasted food. Incentive-based policies, such as tax exemption for donations of specified food items (e.g., dates, rice, wheat, fruits, vegetables, and red/white meat), alongside punitive measures like penalties for wasteful disposal, can effectively reduce food waste. In reality, transparent and enforced regulations alter behavior more consistently than awareness campaigns alone. Furthermore, the results align with Saudi Arabia's Vision 2030 objectives to promote the circular economy in order to foster sustainable agricultural practices that mitigate surplus food waste and bolster food security.

The study suggests that further research should be done on how the circular economy can be used with certain types of food waste and that surveys should be done with consumers about how they shop, cook, and store food. This study would give us reliable information about how people in Saudi Arabia use and throw away things, as well as how successfully they can recycle. The Saudi National Program to Reduce Food Loss and Waste has chosen dates, rice, wheat, fruits, vegetables, and red and white meat as high-priority items for policy intervention. These items are also the ones the program wants to focus on. By finding out how Saudi customers store, prepare, and handle different types of food, researchers can find the exact times when waste is created (for example, when they over-portion, don't refrigerate enough, or misread expiration labels). With this in-depth understanding, CE interventions (including composting, upcycling, and food donation) can be designed and targeted to be more effective.

Also, policymakers will be better able to figure out how to use the circular economy (CE) in real life if they look at a range of factors, such as socioeconomic factors and behavioral factors like how people buy and cook food. This would help people make smart choices that would reduce food waste, make food more secure, and make food more sustainable.

Appendix A.1

Table A1. Results of internal consistency for the items used to construct the CE level using Cronbach's Alpha.

Recycling Food items	Items used to construct the CE level	Cronbach's Alpha if Item Deleted	Cronbach's Alpha coefficient
Bread	Discarding it in the trash.	0.935	0.931
	Converting stale bread into breadcrumbs.	0.930
	Creating croutons for salads or soups, or by preparing nutritious snacks.	0.929
	producing an assortment of confections.	0.928
	Prepare bread soup or thareed, which consists of bread pieces.	0.929
	Utilize it to absorb the odor of scorched rice.	0.928
	Utilize it to preserve the freshness of some food items, like sugar containers.	0.929
	Utilize it for feeding the birds.	0.929
Dates	Discarding it in the trash.	0.934
	Utilize it as fodder for livestock.	0.929
	Refrigeration to prolong their shelf life for later use.	0.930
	Distributing it as gifts to family and neighbours.	0.929
	Distributing it to non-profit societies.	0.929
	Producing an assortment of confections or winter recipes.	0.929
Cooked Rice	Discarding it in the trash.	0.934
	Create additional recipes.	0.928
	Save it to consume the next day.	0.929
	Utilize it for feeding the birds.	0.928
	drying it for decorative purposes and writing on boards.	0.929
Cooked Pasta	Discarding it in the trash.	0.933
	Utilized in preparing salads.	0.928
	Preparing a baked pasta pie	0.929
	Placing it in airtight containers after drizzling oil to use it for the next day	0.928
Stewed vegetables	Discarding it in the trash.	0.933
	Utilize for other recipes, such as pizza	0.927
	Utilize it in soups or stews.	0.927
	Mixed with tomatoes to create sauces.	0.926
	Storing it in the refrigerator for the following day	0.927
Stewed meat	Discarding it in the trash.	0.933
	Prepare a meat pie in the oven.	0.926
	Utilize it to prepare soup	0.926
	Refreezing meat remnants	0.926
	Utilize it for feeding the cats	0.927
Fruits	Discarding it in the trash.	0.933
	Utilize it to prepare both cold and hot beverages.	0.927
	Freezing for future culinary preparations.	0.928
	Preserving in a dried state for year-round consumption.	0.927
	Produce several varieties of jam.	0.927
	Utilize the seeds to cultivate fruit plants to satisfy the family requirements.	0.927
	Utilize leftover fruit as fertilizer for houseplants.	0.926

Table A1. Results of internal consistency for the items used to construct the CE level using Cronbach's Alpha.

Recycling Food items	Items used to construct the CE level	Cronbach's Alpha if Item Deleted	Cronbach's Alpha coefficient
Bread	Discarding it in the trash.	0.935	0.931
	Converting stale bread into breadcrumbs.	0.930
	Creating croutons for salads or soups, or by preparing nutritious snacks.	0.929
	producing an assortment of confections.	0.928
	Prepare bread soup or thareed, which consists of bread pieces.	0.929
	Utilize it to absorb the odor of scorched rice.	0.928
	Utilize it to preserve the freshness of some food items, like sugar containers.	0.929
	Utilize it for feeding the birds.	0.929
Dates	Discarding it in the trash.	0.934
	Utilize it as fodder for livestock.	0.929
	Refrigeration to prolong their shelf life for later use.	0.930
	Distributing it as gifts to family and neighbours.	0.929
	Distributing it to non-profit societies.	0.929
	Producing an assortment of confections or winter recipes.	0.929
Cooked Rice	Discarding it in the trash.	0.934
	Create additional recipes.	0.928
	Save it to consume the next day.	0.929
	Utilize it for feeding the birds.	0.928
	drying it for decorative purposes and writing on boards.	0.929
Cooked Pasta	Discarding it in the trash.	0.933
	Utilized in preparing salads.	0.928
	Preparing a baked pasta pie	0.929
	Placing it in airtight containers after drizzling oil to use it for the next day	0.928
Stewed vegetables	Discarding it in the trash.	0.933
	Utilize for other recipes, such as pizza	0.927
	Utilize it in soups or stews.	0.927
	Mixed with tomatoes to create sauces.	0.926
	Storing it in the refrigerator for the following day	0.927
Stewed meat	Discarding it in the trash.	0.933
	Prepare a meat pie in the oven.	0.926
	Utilize it to prepare soup	0.926
	Refreezing meat remnants	0.926
	Utilize it for feeding the cats	0.927
Fruits	Discarding it in the trash.	0.933
	Utilize it to prepare both cold and hot beverages.	0.927
	Freezing for future culinary preparations.	0.928
	Preserving in a dried state for year-round consumption.	0.927
	Produce several varieties of jam.	0.927
	Utilize the seeds to cultivate fruit plants to satisfy the family requirements.	0.927
	Utilize leftover fruit as fertilizer for houseplants.	0.926