Leveraging Household Food Waste Consumer Behaviour to Optimise Logistics

Abstract

Background: This study explores how consumer behaviour influences household food waste and its ripple effects on the efficiency of the agri-food supply chain. Methods: Using survey data, we applied regression analysis to analyse the links between shopping habits, household demographics, waste reduction goals, and disposal practices. Results: Results show that purchasing driven by promotions significantly boosts household waste, while waste reduction goals strongly reduce disposal behaviours. These results illustrate how irregular consumer purchasing patterns create upstream demand fluctuations, making inventory management and production planning more complex. The findings highlight opportunities for logistics improvements, such as demand-based inventory systems, optimised purchasing routines, adjusted promotional strategies, and consumer-involved forecasting models to cut waste and promote resource sustainability. Conclusions: This research connects consumer behaviour with supply chain management, offering practical insights for building more sustainable and efficient food supply chains through targeted logistics actions.

1. Introduction

Food waste has emerged as a significant global issue with social, ethical, environmental, and economic consequences [1]. Approximately one-third of all food produced for human consumption—about 1.3 billion tons annually—is wasted [2]. This paradox arises at a time when global society oscillates between scarcity and abundance, particularly in Western nations where food availability exceeds demand: European countries produce roughly three times more food than necessary, and the United States nearly four times as much [3].

Recognising the impact of food waste on the planet, the United Nations introduced Agenda 2030, a strategic plan for sustainable development [4]. Among its 17 goals, Goal 12 focuses on responsible consumption and production, calling for countries to halve per capita food waste at the retail and consumer levels, as well as reduce waste throughout production and supply chains. This goal also emphasises the efficient use of natural resources, closely tied to the uneaten food and its associated environmental impact. Related goals include zero hunger, food safety, and water resource management, all of which are affected by global food waste patterns [5].

The European Union is aligned with these objectives, having contributed to their formulation and committed to implementation [6]. Projects such as FUSIONS (2013–2016) have aimed to investigate and address food waste through collaborative, interdisciplinary research [7].

This challenge becomes even more urgent as the global population is expected to surpass 9 billion by 2050. At the same time, rising affluence in developing nations is shifting diets from plant-based foods to more resource-intensive animal products, further amplifying food production demands and potential waste [8]. Yet, as demand rises, natural resources remain limited. Studies estimate that food production must increase by 25% to 75% by 2050, resulting in proportional increases in resource use and potential waste [9].

In response, academic and policy focus has increased since the early 2000s. Governments, researchers, and international organisations are expanding efforts to understand and reduce food waste. The issue is no longer viewed solely through environmental or moral lenses—it is now integrated into broader frameworks such as sustainability, circular economy, consumer behaviour, and food security [10].

Despite this global momentum, Greece falls behind in waste management infrastructure, data availability, and public awareness. According to relevant studies [11], Greece ranks among the least developed EU nations in these areas. The Voluntary National Review of Greece (2022) offers only a brief reference to food waste, stating a national goal to reduce waste by 30% by 2030 and improve measurement and awareness [12]. Still, Eurostat data (2020) indicates that Greece ranks fourth in household food waste per person in the EU, highlighting the urgent need for action and further investigation.

Food waste is not just a consumer problem—it shows inefficiencies throughout the entire agri-food supply chain [13]. From agricultural production and post-harvest handling to processing, distribution, and retail, each stage presents critical points where resources may be lost or wasted. Poor demand forecasting, inadequate storage infrastructure, excessive packaging, and stringent retail standards often result in food being discarded before it reaches the consumer [14]. These systemic inefficiencies accumulate across the chain, ultimately exacerbating the burden on households and masking opportunities for upstream intervention.

Incorporating consumer behaviour insights into agri-food logistics presents a unique opportunity to comprehensively tackle food waste [15]. Understanding why consumers discard food—whether due to over-purchasing, poor planning, or confusion over expiry labels—can inform upstream logistics decisions, such as adjusting inventory replenishment cycles, modifying promotional strategies, or redesigning packaging for smaller, more usable portions [16]. This systems-level approach ensures that waste prevention is not treated as an isolated consumer responsibility but as a shared objective across the food supply network [17].

This study aims to address that gap by examining the food waste behaviour of Greek households and identifying behavioural, demographic, and attitudinal factors that contribute to this issue. More importantly, it seeks to connect these insights to logistics and supply chain processes, highlighting where interventions like better inventory planning, smarter packaging, and targeted education can help reduce waste and improve system efficiency. Additionally, this research places household food waste within a broader supply chain context, linking consumer habits with supply chain management. By connecting consumer behaviour to logistics strategies, this work contributes not only to the literature on food waste but also to the development of more sustainable and resilient food supply chains.

2. Literature Review

A growing body of interdisciplinary research investigates food waste across environmental, economic, social, and behavioural dimensions [18]. As understanding of the issue evolves, it becomes clear that food waste is not only a consequence of inefficiencies in agricultural production or retail operations, but also stems from complex, everyday behaviours at the household level. Consumer decision-making, attitudes, and habits play a key role in driving demand fluctuations and overconsumption, which in turn affect supply chain performance [19,20]. To design effective interventions, it is essential first to define what constitutes food waste, examine the behavioural and demographic patterns associated with it, and then connect these patterns to logistics strategies that can tackle upstream inefficiencies. This section reviews the key literature in these areas.

2.1. Defining Food Waste and Its Scope

Despite its widespread recognition, food waste remains inconsistently defined across academic and policy frameworks. In many cases, the term overlaps with “food loss,” though distinctions are typically drawn based on the stage at which the loss occurs. Food loss generally refers to wastage during early supply chain stages, such as harvesting, post-harvest handling, storage, and transportation, while food waste is more commonly used to describe the discard of edible food at the retail or consumer level [21].

Ambiguities also arise around what is considered edible or inedible. Items like bones or fruit peels may be excluded in some studies and included in others [22]. These distinctions often depend on local dietary customs, which vary widely across cultures. Furthermore, researchers continue to debate whether food repurposed for animal feed, compost, or bioenergy should be classified as waste [23,24]. These inconsistencies create challenges for reliable measurement and cross-national comparisons, complicating both policy development and the assessment of intervention outcomes.

2.2. Household Food Waste: Behavioural and Demographic Factors

Among all stages of the food supply chain, the household sector is widely recognised as the most significant contributor to food waste in high-income economies [25]. Behavioural factors such as inadequate meal planning, poor shopping discipline, misinterpretation of expiry dates, and lack of proper food storage often lead to the unnecessary disposal of edible items. Consumers may also over-prepare food or fail to consume leftovers, resulting in preventable waste [17].

Demographic factors are strongly linked to household food waste. Research consistently shows that younger individuals, those with higher income levels, and people living in smaller households tend to generate more food waste [26]. This may be attributed to increased purchasing power, lower price sensitivity, and less ingrained habits of conserving resources.

To better understand the psychological underpinnings of food waste behaviour, several studies apply the Theory of Planned Behaviour (TPB) [15]. According to this model, consumer intention not to waste food is influenced by personal attitudes, perceived behavioural control, and subjective social norms. While many consumers express strong intentions to reduce waste, these intentions often fail to materialise in practice [27]. The gap between intention and action highlights external barriers, such as time constraints, convenience-based consumption, and insufficient awareness, underscoring the limitations of behavioural intention as a predictor of actual food management practices.

2.3. Linking Food Waste to Logistics and Supply Chain Efficiency

While food waste has traditionally been framed as a sustainability or ethical concern, its implications for logistics and supply chain management are equally profound [28]. Upstream inefficiencies, such as poor demand forecasting and inconsistent traceability, make supply chains vulnerable to disruptions, resulting in supply misfires and, consequently, higher food waste rates [29]. These systemic inconsistencies distort consumer purchasing signals, leading to overstocked shelves and ultimately excess at the household level [30]. Recent studies also underline the interconnection between consumer-centric traceability in agri-food systems and end-to-end supply chain responsiveness to shifting preferences [31,32]. When upstream actors in the supply chain fail to align production and logistics strategies with actual household demand patterns, retailers often resort to aggressive promotional tactics to manage surplus inventory, encouraging excessive consumer purchasing behaviour, which ultimately contributes to increased levels of household food waste [33].

Integrating behavioural insights such as household consumption patterns into logistics design is essential. For example, packaging formats tailored to typical household sizes, dynamic reordering systems, and real-time demand sensing can help stabilise stock flow along the chain [34]. Most research remains polarised between consumer psychology and supply chain optimisation, with limited interdisciplinary integration.

Despite growing interest, very few empirical studies explicitly link consumer behaviour with logistics performance. Much of the existing literature remains isolated, focusing either on consumer behavioural patterns or supply chain optimisation, with minimal cross-pollination between the two. This presents a valuable opportunity to link upstream logistics with end-user outcomes, thereby enhancing systemic analysis of the factors that contribute to food waste. Such an approach enables the identification of strategic interventions that address waste generation at its source, rather than solely at the point of disposal.

2.4. Research Gap and Contribution

Despite the variety of literature exploring food waste from environmental, behavioural, and logistical perspectives, an apparent disconnection remains between these domains—particularly in the empirical linkage between household consumer behaviour and upstream logistics processes. Most studies focus either on behavioural determinants of waste at the household level or on operational inefficiencies in food supply chains, addressing these areas independently. As such, there is limited understanding of how consumer-level demand irregularities indirectly shape supply chain planning and inventory management strategies. His divided approach constrains the development of comprehensive interventions capable of addressing the structural origins of waste generation.

Moreover, existing studies that address the role of logistics in food waste often centre on technological or process-driven solutions, such as cold chain improvements, better packaging, or redistribution networks [35,36]. While these are critical, they tend to overlook the causes that drive demand variability and influence retailer decision-making [37]. Few studies consider how behavioural interventions can be used not only to reduce waste at the consumer level but also to create more predictable demand flows across the supply chain. The lack of integrated behavioural-logistics models represents a critical blind spot in current food waste reduction efforts.

Drawing on primary data, this study identifies key behavioural drivers of household food waste that send irregular demand signals throughout the supply chain. These findings reveal opportunities for targeted logistics interventions, including adaptive inventory strategies and demand-responsive packaging, which can enhance both waste prevention and supply chain resilience. By leveraging consumer-level insights, this study provides insights that can inform decision-making across all stages of the agri-food supply chain, ultimately promoting more sustainable and efficient operations [38].

3. Materials and Methods

To measure food waste in households, Koivupuro et al. [26] identify two fundamental approaches. The first involves specialised companies that collect all the food discarded by a household in a single day in containers. These are then analysed, sorted, and weighed. While this method provides precise, accurate, and objective measurements, it is time-consuming and expensive. The second method relies on questionnaires completed by the households themselves and is the most commonly used in existing research. This study will utilise the second method, gathering data directly from consumers through a structured questionnaire. To test the hypotheses, the following regression model was employed:

HFWi = β0 + β1PFPi + β2PrFPi + β3IRWi + β4SDCi + ε_i

(1)

where HFWi denotes household food waste, PFPi price-focused purchasing, PrFPi promotion-focused purchasing, IRWi intention to reduce waste, and SDCi the socio-demographic controls (age, income, household size). The coefficients (β) represent the strength and direction of the relationships, and ε_i is the error term.

The analytical model employed is simple linear regression, a tool also used in the related research [30]. Statistical processing will be conducted using SPSS 27, a widely used statistical software in studies of this nature. Reliability of the questionnaire items that represent the variables will be tested using Cronbach’s alpha coefficient, as has been carried out in the aforementioned studies on food waste [15,26,39,40].

Next, the significance level (p-value) of the relationships under examination will be assessed. Pearson’s correlation coefficient will also be calculated to determine the direction and strength of the relationships between variables, consistent with the methods used in previous studies [15,26,39,40]. For those relationships found to be statistically significant, the regression coefficient (b) and the coefficient of determination (r²) will also be computed.

3.1. Hypotheses

To determine how consumers make food purchasing decisions, we categorised them into two groups: those motivated by the lowest price and those driven by promotional offers. Their research found that the more committed consumers were to low prices and promotions, the less food they tended to waste at home—an outcome that contradicts several other studies.

Regarding promotion-driven purchases, a study conducted in a well-known American food retail store provided customers with a coupon for a free item with their purchase. Measurements later showed that the volume of food waste in these households increased after using the coupon [41]. In Australia, Wharton, Foth, and Choi [42] conducted a study focusing on promotional purchases, such as “buy one get one free” offers or buying larger quantities for a discount. The results indicated that consumers who mainly purchased food in bulk—where larger quantities came at a lower price—ended up discarding more, often due to expiration. A prominent example involved tomatoes: in their attempt to obtain a discount, consumers bought more than they needed, and the unused portion spoiled before it could be cooked. Similarly, a Swiss study involving 165 households found that those purchasing promotional items—either due to imminent expiration dates, bulk discounts, or general price reductions—accumulated larger quantities of food waste [43]. Schneider [44] focused on the impact of low-price promotional purchases. His results showed that consumers who concentrate on these types of purchases often engage in over-buying, ultimately leading to more waste.

While these international studies shed light on consumer behaviour patterns, they rarely explore how such behaviours impact upstream supply chain planning, demand forecasting, or inventory management. As such, this study extends the analysis by using these behavioural insights to reflect on systemic logistical consequences.

Given the lack of relevant research specific to the national context, this study draws on international findings. Most of the referenced studies suggest a positive correlation between purchasing food based on price promotions or low cost and increased household food waste. Accordingly, the following hypotheses are proposed:

H1: 

Food purchases driven by low prices are positively associated with household food waste.

H2: 

Food purchases driven by promotional offers are positively associated with household food waste.

Regarding consumer empathy toward the food waste issue, the literature often attempts to assess this through their intention to prevent it. A 2021 study found that the stronger the consumer’s intention to reduce waste, the less food was discarded [39]. Similarly, a study in the Netherlands, involving a sample of 211 households, found that the intention to avoid food waste resulted in lower actual waste generation [45]. A similar conclusion was reached in a 2023 study in Thailand, where a negative and statistically significant correlation was observed between the intention to reduce waste and the actual volume of food waste [15]. This leads to the formulation of the following hypothesis:

H3: 

The intention to prevent food waste is negatively associated with household food waste.

In terms of socio-demographic characteristics, the literature has identified several key variables that influence the amount of food waste generated, notably income, age, and household size. Regarding the income, there is clear evidence in the literature of a positive correlation with food waste. Examples include studies in Finland [30] and Romania [40], all of which indicate that higher-income households are more prone to discarding food. A survey in Athens by Abeliotis, Lasaridi, and Chroni [46] also confirmed that lower-income households produced significantly less waste compared to wealthier ones. Further support comes from research by Barrera and Hertel [47], who examined food waste behaviour among low- and high-income households in China. They found that low-income households generated minimal waste, as they lacked the economic flexibility to waste food. However, as income increased, so did the volume of discarded food. Thus, the sub-hypothesis is:

H4A: 

Income is positively associated with household food waste.

Another widely acknowledged factor is age. A 2018 study in Romania found that younger individuals adopted consumption habits that led to higher food waste [48]. Similarly, Tsalis et al. [30] found that younger consumers in Denmark generated more waste than older ones. Therefore:

H4B: 

Age is negatively associated with household food waste.

Household size is also frequently cited in the literature. A study in Asia involving 1100 households showed that the probability of generating large volumes of waste increased significantly when the number of household members exceeded three [49]. This finding is consistent with earlier studies, leading to the final sub-hypothesis:

H4C: 

Household size is positively associated with household food waste.

To summarise, the final hypothesis is:

H4: 

Socio-demographic characteristics influence household food waste.

H4A: 

Income is positively associated with household food waste.

H4B: 

Age is negatively associated with household food waste.

H4C: 

Household size is positively associated with household food waste.

The hypothesised relationships are illustrated in the conceptual model (Figure 1).

3.2. Questionnaire Design

A review of the relevant literature reveals that the sample size used in household food waste studies based on questionnaires typically ranges from 200 to 1500 participants [15,26,39,40]. In the present study, the target sample is 200 participants.

The inclusion criteria for participation require that respondents reside within the Attica region. As in similar studies by Abeliotis et al. [50], only individuals with some responsibility for household food waste are eligible. This is assessed through two specific questions included in the questionnaire: the extent to which participants are responsible for cooking and for food shopping in their household. Responses are based on a five-point Likert scale, where 1 indicates “Not at all” and 5 indicates “Completely.” Participants who respond with “1” to both questions are excluded from the analysis.

The core structure of the questionnaire consists of four groups of questions, each corresponding to a central variable in the study. The purpose of this structure is to allow for quantitative analysis of these variables and to test the correlations proposed in the hypotheses. All questions are adapted from previously published studies that examined the same variables and have undergone preliminary validation in terms of consistency and completeness. Additional socio-demographic questions are also included.

To measure the dependent variable—household food waste—participants are asked to indicate the extent to which they agree with a series of statements that reflect real-life behaviours. These include difficulties in consuming leftovers stored from previous meals, the discovery of unopened food packages that must be discarded, and the tendency to leave promotional food items uneaten. Other questions explore behaviours such as intentionally overeating to avoid throwing food away or discarding food simply because it has reached its expiration date or has spoiled. These items were adapted from the study by Tsalis et al. [30].

The independent variable related to price-conscious purchasing behaviour is assessed through statements that reflect attitudes and habits toward seeking the lowest prices. Participants respond to items that determine whether they perceive the savings from finding the lowest prices as worth the time and effort, whether they are willing to visit multiple stores for better prices, and whether they compare prices during shopping.

Similarly, the variable related to purchasing behaviour based on promotional offers is measured through statements indicating a preference for waiting until food is discounted, actively searching for promotional items during shopping, and a tendency to buy larger quantities when food is on offer to stock up. These behaviours are likewise drawn from the same prior research.

Finally, the independent variable related to consumer empathy and the intention to avoid food waste is measured through questions focused on planning and self-regulation behaviours. Participants are asked whether they intend to avoid wasting any edible food, whether they check their food inventory before shopping, and whether they prepare shopping lists and weekly meal plans. The extent to which they aim to cook only the necessary quantity of food for their household is also assessed. These items are taken from Kritikou et al. [39].

4. Results

4.1. Sample Characteristics

The questionnaire was distributed via email, social media platforms such as Facebook, and through personal interviews. The questionnaire was distributed through Facebook via the lead researcher’s personal timeline and targeted community groups focused on household consumption behaviours, following established social media recruitment protocols. Data collection extended over six weeks, achieving organic reach through network effects and group sharing. This approach is validated in recent consumer behaviour research as an efficient method for accessing diverse urban populations [51]. A total of 212 responses were collected. Of these, 30 were excluded because the respondents resided outside the Attica region, and five were excluded because they reported no interaction with food within their household (indicated by selecting “1” on both relevant questions). Consequently, the final sample used for the statistical analysis consisted of 177 individuals.

Regarding the demographic statistics, less than 1% of the sample was aged 15–19 years, 62% were aged 20–29 years, 19% were aged 30–39 years, 10% were aged 40–49 years, 7% were aged 50–59 years, and less than 1% were aged 60–69 years. Most respondents fell within the 20–29 age group. This can be attributed to the fact that many participants were recruited via Facebook, a platform predominantly used by this age demographic. This overrepresentation poses a limitation for statistical analysis, as a more balanced age distribution would yield more robust results.

Regarding gender, 67% of the participants were women and 33% were men. Regarding household size, the sample distribution was well-balanced. Single-person households made up 14% of the sample, two-person households 36%, three-person households 20%, and households with four or more members accounted for 30%.

As for annual income, responses were also evenly distributed. In the income bracket up to €5947, there were 32 respondents (18% of the sample); in the €5948–€8752 bracket, there were 38 respondents (21%); in the €8753–€12,308 bracket, 53 respondents (30%); and in the highest bracket, over €12,308, 54 respondents (31%). All these results are described in

Table 1. Demographic characteristics of the sample (N = 177).

Variable	Category	Percentage (%)
Age	15–19	<1
	20–29	62
	30–39	19
	40–49	10
	50–59	7
	60–69	<1
Gender	Male	33
	Female	67
Household size	1 person	14
	2 persons	36
	3 persons	20
	4+ persons	30
Annual Income (€)	≤5947	18
	5948–8752	21
	8753–12,308	30
	>12,308	31

.

4.2. Reliability

The first step in the statistical analysis was to calculate Cronbach’s alpha for each of the variables under study. This measure of internal consistency is essential for determining whether the items in a scale reliably measure the same construct. The relevant results are presented in

Table 2. Cronbach’s alpha coefficients.

Variable	No of Items Included	Coefficient of Cronbach’s Alpha	Reliability Level
Household food waste generation	4	0.857	Good
Price-focused purchasing	4	0.195	Unacceptable
Promotion-focused purchasing	4	0.868	Good
Intention to reduce food waste	5	0.882	Good

.

For the dependent variable, household food waste generation, the Cronbach’s alpha was 0.857 across four items, indicating good reliability. The items included discarding leftovers from previous meals, finding and discarding expired packaged products in the pantry, not consuming discounted food products after purchase, and discarding the last portion of a food item due to spoilage or expiration.

For the variable related to price-focused purchasing, the Cronbach’s alpha was 0.195 across four items, indicating poor reliability. These items included statements about not valuing the time spent searching for lower prices and the willingness (or lack thereof) to search multiple stores or compare prices. The low reliability may stem from the negatively phrased wording of the first two items, which may have confused participants.

For promotion-focused purchasing behaviour, Cronbach’s alpha was 0.868 across four items, reflecting strong internal consistency. The items assessed the tendency to seek discounted products, delay purchases until a promotion appears, buy larger quantities during offers, and stockpile promotional goods.

For the variable measuring intention to reduce food waste, the Cronbach’s alpha was 0.882 across five items, also indicating high reliability. These items evaluated behaviours such as checking food inventory before shopping, making grocery lists, planning meals, and cooking appropriate quantities.

In summary, the dependent variable—household food waste generation—demonstrated acceptable internal consistency with a Cronbach’s alpha of 0.857. Two independent variables, promotion-focused purchasing and intention to reduce food waste, were also deemed reliable with alpha values of 0.868 and 0.882, respectively. The variable concerning price-focused purchasing, however, was not considered reliable due to its low alpha score of 0.195, suggesting the items did not measure the same underlying construct.

4.3. Hypothesis Testing

Regarding the relationship between household food waste and promotion-focused purchasing behaviour, a statistically significant correlation was observed with a p-value < 0.001. Pearson’s correlation coefficient was 0.478, indicating a moderate-to-strong positive association. The R² value of 0.228 indicates that 22.8% of the variance in household food waste can be attributed to the tendency to purchase food on promotion. The regression coefficient (β) was 0.476, suggesting that for every unit increase in promotion-focused purchasing behaviour, household food waste increases by 0.476 units. This result aligns with previous findings by Wharton et al. [42], Wu and Honhon [41], and Schneider [44], confirming hypothesis H2, which posits a positive correlation between purchasing behaviour focused on promotions and household food waste. The statistical outcomes are also summarised in

Table 3. Summary of hypothesis testing results.

Hypothesis	Dependent Variable	Independent Variable	Pearson’s r	R2	β Coefficient	p-Value	Result
H2	Household food waste	Promotion-focused purchasing	0.478	0.228	0.476	<0.001	Supported
H3		Intention to reduce food waste	−0.559	0.312	−0.450	<0.001	Supported
H4A		Income	0.062	0.004	0.202	0.808	Rejected
H4B		Age	0.036	0.001	−0.179	0.634	Rejected
H4C		Household Size	0.042	0.002	0.142	0.579	Rejected

.

Regarding the first hypothesis (H1), no statistically valid conclusions could be drawn due to the unreliability of the scale used to measure price-focused purchasing. Specifically, the Cronbach’s alpha coefficient for this variable was 0.195, well below the accepted threshold for internal consistency. This low reliability suggests that the four items intended to measure this construct did not capture a coherent underlying attitude or behaviour. One likely explanation lies in the structure of the items: two of the statements were negatively worded, which may have caused confusion or misinterpretation among respondents, thus disrupting the scale’s internal alignment. As a result, the responses may not reflect a consistent behavioural pattern regarding low-price purchasing strategies. Given these measurement issues, hypothesis H1 must be rejected, and future research should consider revising the scale—either by rephrasing or replacing the problematic items—to ensure clearer conceptual coherence and improved respondent comprehension. This outcome is reflected in Table 3.

In the second model, the relationship between household food waste and consumers’ intention to reduce it also showed statistical significance with a p-value < 0.001. Pearson’s correlation coefficient was −0.559, indicating a strong negative association. The R² value of 0.312 implies that 31.2% of the variance in household food waste is explained by consumers’ intention to avoid waste. The regression coefficient (β) was −0.450, indicating that each unit increase in waste-reducing intention decreases the tendency to generate household food waste by 0.450 units. These figures are consistent with existing literature, including Kritikou et al. [39], who examined Greek households, as well as studies conducted in Thailand [15] and the Netherlands [45], thereby supporting hypothesis H3. This finding is also presented in Table 3.

Regarding Hypothesis H4, which explored whether socio-demographic characteristics—specifically household size, income, and age—are associated with household food waste, the analysis did not yield statistically significant results. The p-values for household size (p = 0.579), annual income (p = 0.808), and age (p = 0.634) were all well above the 0.05 threshold, indicating that no meaningful correlations were detected in the sample. This outcome is somewhat unexpected, given that numerous previous studies have identified significant associations between these variables and food waste levels [30,40,44,48]. Several possible explanations may account for this discrepancy. First, the sample was skewed toward younger respondents, particularly those aged 20–29, which may have limited the variability required to observe age-related trends. Second, the income distribution in the sample was relatively even, potentially masking the economic constraints or excesses typically associated with waste behaviour. Lastly, cultural or regional dynamics specific to Attica may moderate the influence of these socio-demographic variables, reducing their statistical relevance. Despite the lack of significance, these findings raise important questions about the generalisability of demographic predictors across different contexts and suggest a need for more nuanced investigation into how socio-economic factors interact with food waste behaviours. Therefore, Hypothesis H4 is rejected. The statistical results are summarised in Table 3. A graphical summary of the hypothesis testing results, including directions of the relationships and statistical values (r, β, R², p-values), is presented in Figure 2.

5. Discussion

The results of this study shed light on the behavioural mechanisms that contribute to household food waste and reveal critical implications for supply chain efficiency and logistics interventions across the agri-food system.

One of the most significant findings was the strong positive link between promotion-based purchasing behaviours and household food waste. Consumers who often bought products on promotion—such as “buy one get one free” offers or bulk discounts—were much more likely to throw away food. This supports earlier evidence that promotional strategies can cause over-purchasing, especially of perishable items, which often go beyond actual consumption needs. When storage options or meal planning are lacking, these extra goods are more likely to spoil. From a supply chain viewpoint, this behaviour creates artificial demand spikes that ripple upstream, leading to supply inconsistencies and higher resource use. Therefore, incorporating demand-side behavioural data into retailer forecasting systems or promotion planning could help reduce inefficiencies not just for consumers but throughout the entire food supply chain.

Equally important is the strong negative correlation between consumers’ intention to reduce food waste and the actual volume of waste produced. Individuals who engaged in practices such as checking their pantry before shopping, planning meals, and aiming to avoid unnecessary food purchases were markedly less likely to waste food. These behaviours offer valuable entry points for supply chain innovation. For instance, demand-sensitive inventory systems and consumer-facing digital tools, such as apps for meal planning or expiry tracking, could align household consumption more closely with actual need, reducing unnecessary throughput across the supply chain. The connection between informed household decisions and reduced upstream demand pressure underscores the value of aligning logistics systems with consumer behavioural trends.

In contrast, no statistically valid conclusions could be drawn regarding the relationship between household food waste and price-focused purchasing behaviour. The internal inconsistency of this variable (Cronbach’s alpha = 0.195) suggests that the current measurement approach did not effectively capture this behavioural dimension. While not definitive, this result underscores the need for robust psychometric instruments to accurately capture the nuanced drivers of consumer decision-making. From a supply chain perspective, the ambiguity surrounding price sensitivity also highlights a broader need for more accurate data on consumer price-value perceptions, which are crucial to understanding fluctuating purchasing behaviours that can destabilise inventory levels.

Surprisingly, socio-demographic characteristics—namely age, income, and household size—did not show statistically significant links to food waste in this sample. Although earlier research often highlights these variables as important, their lack of influence here suggests that consumer behaviour and intention may be better predictors of food waste outcomes. The overrepresentation of young adults and a relatively even income spread in the sample may have limited the variation needed to identify demographic patterns. Still, this finding supports a shift in perspective: instead of relying solely on demographic segmentation, supply chain actors and policymakers might achieve greater impact by focusing on attitudinal and behavioural patterns. Targeted interventions based on these more dynamic factors can lead to more accurate demand forecasting and waste reduction throughout the supply chain.

The patterns of behaviour found in this study highlight specific opportunities for improving the upstream supply chain, going beyond traditional demand forecasting methods. The 22.8% variation in food waste attributed to promotional purchasing suggests that nearly one-quarter of household waste results directly from promotional strategies employed by retailers and manufacturers, indicating that they bear some indirect responsibility for downstream inefficiencies. This insight calls for the development of “waste-adjusted demand forecasting” methods that differentiate between actual consumption-driven demand and demand inflated by promotions that cause over-purchasing and subsequent disposal. Supply chain participants should incorporate behavioural adjustment factors into their planning systems, using real-time consumer data gathered through IoT sensors, mobile apps, and digital platforms that track meal planning and consumption habits. This integration enables the creation of behaviourally responsive supply chains that automatically adjust inventory levels, promotional strategies, and delivery schedules based on the likelihood of household waste.

Additionally, the significant link between waste reduction intentions and actual waste levels (R² = 0.312) supports the creation of forecasting systems that incorporate household planning behaviours as early indicators of demand stability. Advanced supply chain companies can utilise machine learning algorithms to identify predictable consumption patterns and optimise logistics, while also adopting collaborative approaches such as distributed redemption programmes and dynamic pricing that consider individual behavioural profiles. These technology-driven methods foster mutually beneficial relationships where consumer behaviour tools generate valuable data for upstream efficiency gains, enabling blockchain traceability systems and AI-driven behavioural models that turn food waste management into a proactive supply chain optimisation opportunity.

Overall, the findings support a systems-oriented approach where food waste is viewed not just as a consumer-level issue but as a sign of poor coordination throughout the entire agri-food supply chain. By integrating consumer insights into upstream logistics planning, stakeholders can create interventions that prevent food waste instead of just responding to it. This approach connects behavioural science with supply chain management, helping build more resilient and efficient food systems.

6. Conclusions

This study concludes that consumer purchasing behaviour—specifically, the way individuals shop for food—significantly impacts household food waste. A key finding is the strong positive link between promotional-driven purchases and the tendency to throw away food. Consumers who focus on offers like “buy one, get one free,” bulk discounts, or free-item deals are more likely to dispose of excess food. This suggests that the widespread use of promotional strategies in food retail encourages over-purchasing, leading to unnecessary waste. Such behaviour not only causes economic inefficiencies for households but also increases environmental and societal costs. Importantly, these household-level waste patterns have cascading effects across all stages of the agri-food supply chain. Fluctuating demand caused by consumer over-purchasing creates uncertainty in inventory flow, making production planning, distribution, and stock management more complicated upstream. At the same time, these findings should be viewed cautiously due to limitations in sample composition and the low reliability of the scale used to measure price-focused purchasing.

Conversely, the analysis showed a strong negative link between consumers’ intention to prevent food waste and the actual amount of waste they generate at home. Essentially, the more consumers intend to avoid waste—through meal planning, checking supplies, and mindful shopping—the less food they discard. This emphasises the importance of consumer awareness and self-control in tackling food waste at home. Additionally, these behavioural patterns can provide valuable information for upstream stakeholders, helping them to better adjust logistics strategies and reduce structural inefficiencies.

6.1. Research Contributions

This study makes several contributions to both food waste and supply chain management literature. Firstly, it introduces a set of theoretical advances starting with a behavioural-logistics integration approach that creates a novel conceptual link between household consumer behaviour and upstream supply chain planning, addressing a vital gap in interdisciplinary food waste research. Additionally, it proposes a demand volatility causation concept that empirically demonstrates how specific consumer behaviours (promotional purchasing) generate systematic demand fluctuations that flow upstream, establishing a theoretical basis for behavioural supply chain management. Another key contribution is the waste-efficiency nexus, which enhances understanding of how household-level waste output signals broader supply chain inefficiencies, promoting systems thinking in food waste research.

From a methodological standpoint, the study presents a consumer-supply chain linkage methodology that develops an analytical approach for connecting household behavioural data with supply chain performance implications, serving as a model for future interdisciplinary research. This integrated measurement framework combines consumer behaviour constructs (intentions, attitudes) with supply chain performance indicators, providing a comprehensive assessment tool.

These contributions position this research at the crossroads of consumer behaviour, operations management, and sustainability science, laying a foundation for future interdisciplinary approaches to optimise food systems.

6.2. Implications

Regarding promotions, much of the responsibility lies with food retailers. In Denmark, a global leader in food waste reduction, regulations prohibit “buy one, get one free” offers in food retail, reflecting a structural approach to limiting overconsumption. Another promising intervention is Tesco’s “Buy One, Get One Later” model, which allows consumers to redeem a second item later via a voucher. This strategy helps distribute food purchases over time, minimising the risk of spoilage before consumption [52]. Even relatively simple interventions, such as removing aggressive promotional labels and signage, have been shown to reduce impulsive purchases driven by superficial price incentives [53]. Such retail-level changes not only benefit households but also stabilise demand across the supply chain, improving inventory turnover and minimising upstream surpluses.

From the consumer’s perspective, resisting promotional influence remains challenging. Companies invest heavily in designing offers that exploit behavioural triggers to steer purchase decisions [54]. While public education campaigns can promote more sustainable purchasing habits, further research is needed to understand the psychological and contextual factors that drive promotion-focused shopping. Addressing these knowledge gaps is crucial for developing more effective and targeted interventions. Additionally, future research should explore how real-time supply chain actors can operationalise consumer feedback and behavioural data to enable more responsive and waste-resilient systems.

Regarding consumer empathy and environmental consciousness, future research should investigate the mechanisms by which these values can be cultivated and reinforced. Identifying factors that enhance consumer engagement with waste reduction could prove instrumental in advancing household-level sustainability. Such values may not only change individual behaviour but also improve the effectiveness of upstream interventions by creating more consistent and predictable demand patterns throughout the agri-food network.

6.3. Limitations and Directions for Further Research

This study is not without limitations. Firstly, the sample size was relatively small due to logistical constraints, particularly given the single-researcher nature of the project. Additionally, the study was conducted in Greece, with a primary focus on the urban area of Athens. As such, the findings may not be generalizable to the broader Greek population or other countries. Furthermore, the sample is limited to the Attica region and includes a relatively high proportion of young adults (20–29 years), with income distribution fairly evenly across the group. These characteristics should be considered when considering the generalisability of the findings, particularly those related to H4. The non-significant socio-demographic findings should be interpreted cautiously, given the sampling constraints outlined above. However, these results provide important baseline data for understanding demographic patterns in food waste behaviours within supply chain contexts. Future cross-cultural studies using stratified sampling methodologies could validate these preliminary observations and examine whether socio-demographic factors influence food waste behaviours differently across various national and urban contexts. This approach would strengthen both the statistical power and generalizability of demographic analyses in supply chain research. A further limitation concerns instrument design. The construct of price-focused purchasing displayed low internal reliability (Cronbach’s alpha = 0.195). Therefore, Hypothesis 1 (H1) could not be tested empirically. This does not imply that the hypothesis itself is invalid, but rather that the measurement scale was inadequate. Future studies should refine this construct by rephrasing, reverse-coding, or replacing problematic items to better capture consumer price sensitivity.

Future research could replicate the study across multiple cities and in different national contexts to verify the consistency of these findings across diverse consumer groups. Such comparative studies would also be valuable in identifying how cultural, infrastructural, or regulatory differences shape the interaction between household food waste and supply chain dynamics. Future studies could benefit from pilot testing to refine these items. If these design and sampling issues are addressed and the analysis reproduced, the study’s propositions could be validated more robustly and generalised to a broader consumer group.

Ultimately, expanding this research across contexts and disciplines can strengthen the design of integrated food waste reduction strategies that align household practices with the logistical imperatives of a sustainable agri-food supply chain.