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Intention to Purchase Active and Intelligent Packaging to Reduce Household Food Waste: Evidence from Italian Consumers

Department of Agriculture, Food, Natural Resource and Engineering (DAFNE), University of Foggia, 71122 Foggia, Italy
Author to whom correspondence should be addressed.
Sustainability 2021, 13(8), 4486;
Received: 26 February 2021 / Revised: 14 April 2021 / Accepted: 15 April 2021 / Published: 17 April 2021


Innovations in food packaging, such as active and intelligent ones, improve food safety and lower household food waste by extending product shelf life and providing information about food quality, respectively. The consumer adoption of such innovations could contribute to reaching one of the Sustainable Development Goals which calls for halving the per capita global food waste by 2030. Thus, this paper aims to investigate the consumers’ willingness to purchase active and intelligent packaging to reduce household food waste using a sample of 260 Italian consumers and a modified Theory of Planned Behavior (TPB) model. Using a structural equation model, findings show that respondents are more willing to purchase intelligent packaging rather than active packaging to reduce their wastes at home. Finally, attitudes, perceived behavioral control, awareness, and planning routines are the most important drivers of the intention to reduce household food waste.

1. Introduction

Ensuring food safety is an essential step to guarantee human health and to achieve food security [1] while, at the same time, it inevitably leads to the generation of food waste (FW) [2]. Thus, guaranteeing food safety may hinder the achievement of one of the Sustainable Development Goals (SDGs), adopted by the United Nations’ 2030 Agenda, which is that of halving per capita global FW by 2030 [3,4,5,6]. However, new technology innovations, in the food packaging sector, are able to jointly ensure the safety of food, maintain the expected quality throughout the food supply chain (FSC), and allow lowering the waste generated at the household level by extending the product shelf life or indicating the quality level of the food product, supporting consumers in sustainable food choices [2,3,7]. Thus, the packaging, besides protecting and preserving the food content from contaminations of the external environment and from mechanical damage during transportation stages [8,9], plays a key role in preventing food spoilage, extending its shelf life, and thus minimizing waste [10].
In industrial countries, where 99.8% of all food and beverage items are sold packaged [11,12], improvement in packaging features could be a suitable solution to improve food safety and then to avoid FW [7,13]. In EU-28, around 88 million tons of FW, or 173 kg per capita, are generated annually and almost 53% occurs at the household level [14,15]. In Italy, the total amount of FW is estimated at approximately 20 million tons in 2006, resulting in approximately 146 kg per person of which 27.5 kg occurs at the household level with associated total costs of €12.7 billion [16,17]. Foods which are more frequently wasted are mainly dairy products, eggs, and meat (43%), followed by bread (22%) and fruit and vegetables (19%) [16].
The need to improve food safety and to reduce the amount of FW at final stages of the FSC has led companies to develop innovative packaging solutions such as active and intelligent ones [7]. In detail, active packaging refers to substances added to polymer films or sachets inside the packaging that can absorb (scavengers) or release (emitters) gaseous matter [18,19]. Active packaging such as ethylene scavengers, oxygen scavengers, moisture absorbers, and carbon dioxide emitters have the potential to extend food shelf life while maintaining food quality [7,20,21]. Instead, intelligent packaging such as time–temperature indicators, freshness indicators, and leak indicators provide information to manufacturer, retailer, and consumer about food quality and safety based on the ability to test, detect, or record external or internal changes in the product’s environment [12,22,23,24]. This technology could help retailers and consumers to reduce waste, minimizing the risk of foods still edible being thrown away [7,25]. Active and intelligent packaging are defined in Europe by the Regulation 450/2009/EC, which integrated the Regulation 1935/2004/EC [26]. The European regulation states that the individual substances, or the combination of substances, used to make active and intelligent components should be subjected to the European Food Safety Authority (EFSA) authorization, which performs the risk assessment of such substances. Also, the regulation requires that active and intelligent packaging should be labeled with the words “do not eat”, allowing consumers to identify nonedible parts [11,12,26]. Table 1 lists and describes the main active and intelligent packaging technologies already available on the market.
However, most of the technological research in the food area, including packaging, is consumer-driven, meaning that innovations are successful only if accepted by consumers [33]. For instance, consumers willing to lower FW would also be more willing to accept and to purchase food products packaged in innovative solutions.
To the best of our knowledge, no studies on this topic are available. Then, the aim of this paper is to explore to what extent consumers willing to reduce FW are also willing to purchase products packaged with active and intelligent packaging to achieve their scope, and also, to assess which of these two packaging innovations is most preferred by consumers in order to support food companies in investing in the technological solution more likely to succeed on the market.
The next section outlines the literature review and the proposed empirical framework. Section 2 describes the data and the model used in this analysis. Section 3 presents the results that are then discussed in Section 4. Finally, Section 5 ends the paper with the conclusions based on the findings obtained in our study.

Literature Review on Individual Driver of Lower Food Waste and Theoretical Framework

Previous studies largely used the Theory of Planned Behavior (TPB) introduced by Ajzen (1991) to explain food consumption patterns as well as to analyze FW drivers [34]. Reviewed studies point out that not only individual attitude, subjective norm, and perceived behavioral control, but also individual awareness, food shopping routines, planning routines, and ability to reuse food leftovers, are related to the individual willingness to reduce FW.
Attitude entails the extent to which the individual has a favorable or unfavorable evaluation of the behavior [34]. In the literature, negative attitudes toward FW, such as feeling bad or guilty about wasting food, have a significant role in the consumer intention to not waste food [35,36,37].
Subjective norms indicate the social pressure that the individual can perceive in performing or not performing a certain behavior [34]; individuals should intend to waste less food if their food wasteful behaviors are disapproved of by other members in their personal networks [35,37,38,39,40].
Perceived behavioral control refers to the individual’s perceived ability to perform a specific behavior, and thus the extent to which the individual perceives the behavior to be easy or difficult to enact and be under his/her control [34]. In the literature, perceived behavioral control relates to the degree to which consumers think reducing FW is under their full control. It represents potential barriers to or facilitators of conducting the behavior according to whether the subject perceives ease or difficulty in lowering FW [35,36,37]. Individuals who declare themselves able to lower household waste more likely than others will implement strategies to pursue such a goal, to lower FW.
Also, consumers’ awareness regarding the amount and type of food they waste and its consequences from an economic and an environmental standpoint affects individual intention to lower FW [35,41]. Consumers that record a higher level of awareness about the negative effect of waste will be more likely to reduce household waste [35,36,39,42,43].
Furthermore, shopping and planning routines are recognized to be important in explaining consumer behavior related to household FW [35,40,44]. Negative shopping routines, such as purchasing too much food during shopping trips, could contribute to increased household waste [35,39,44]. Similarly, planning meals in advance or making a shopping list may contribute to a lower household FW [35,40,42,44,45]. Also, leftovers reuse routines could further contribute to lower household waste [35,44]. Therefore, good shopping, planning routines, and good individual ability to reuse leftovers may positively impact consumers’ intention to reduce household waste.
These factors listed above that literature found to affect individual intention to reduce household FW may also relate to individual willingness to purchase active or intelligent packaging in attempting to further mitigate the household waste. Figure 1 shows the empirical link between the variables described above.

2. Materials and Methods

2.1. Participants and Design

Data were collected by means of a web-based survey conducted in April 2020 in Italy. The survey collected information on Italians aged 18 years old and over, who were responsible for the food shopping in their household. Before starting the survey, a brief description of active and intelligent packaging was provided to respondents, as reported in Table A1 in Appendix A. In this study, we used a convenient sample composed of 260 respondents, whose sociodemographic and economic characteristics are reported in Table 2.

2.2. Measures

The questionnaire contained measures of attitudes towards food waste, subjective norms, perceived behavioral control, awareness of food waste consequences, food-related routines, intention to reduce household FW, willingness to purchase active and intelligent packaging, and sociodemographics. Generally, respondents were asked to indicate their agreement or disagreement to some statements scored on a seven-point Likert item scale ranging from “totally disagree” (1) to “totally agree” (7).
Following the TPB (Ajzen, 1991), a measure of general attitudes toward FW was used, consisting of a single-item scale: “Throwing away food is an irresponsible behavior”.
Subjective norms were measured with a two-item scale: “Most people important to me disapprove of me throwing out some food” and “Wasting food makes me feel guilty (e.g., for people who do not have enough food, for the environment, for the waste of money)”. These statements were developed in accordance with the TPB and with prior literature on household FW [35,44].
Moreover, perceived behavioral control was assessed with a two-item scale: “Household food waste is avoidable” and “Reducing household food waste helps in solving waste issues” [35,45,46,47].
Consumers’ awareness of the economic, social, and environmental consequences derived from the generation of household FW was measured with a two-item scale. The first one captured the economic awareness and it was assessed with the following statement: “The amount of food waste generated at home is a significant waste of money”. It was formulated in compliance with prior FW literature [35,41,44]. The second item, related to the consumer’s awareness of social and environmental issues, was verified as follows: “The amount of food waste generated at home is a very important social and environmental problem” [47].
Shopping routines were measured with a three-item scale which referred to buying more food than necessary: “I often buy unintended food products when shopping”, “I often buy food in packages that are too big for my household’s needs”, and “I usually buy higher amounts of food when there are special offers”. Planning routines were, instead, assessed with a two-item scale related to shopping and meals: “The shopping trips are usually planned in advance (e.g., shopping lists are made)” and “The home meals are usually planned before going to the grocery store”. Finally, routines related to leftovers were identified with a single-item scale about the storage of leftovers, as follows: “The leftovers are stored in appropriate conditions so they will be consumed later”. All these items related to shopping routines, planning routines, and leftovers reuse routines were developed by the authors based on previous studies of consumer FW [35,44].
The intention to reduce household FW was measured using a three-item scale: “I am not interested in reducing household food waste and I have not planned to reduce it in the next month”, “I am interested in reducing household food waste and I have planned to do so in the next month”, and “I am interested in reducing household food waste and I have already started to do so in the last month” [48].
Finally, to measure the willingness to purchase active and intelligent packaging, respondents were asked to indicate their intentions with a seven-point Likert item scale ranging from “totally not willing” (1) to “totally willing” (7), related to these two statements: “Are you willing to purchase food products packed with active packaging?” and “Are you willing to purchase food products packed with intelligent packaging?” Lastly, the mean value was calculated for the elements of TPB measured by using a multiple-item scale as shown in Table A2 in Appendix A. The latter also shows the correlations between all the variables considered in the proposed empirical framework.

2.3. Estimation Method

The model in Figure 1 has been estimated using structural equation modeling (SEM), which simultaneously includes measurement and structural parameters in a full latent variable model approach. The measurement model is related to the within-construct relationship, which regards the relation among measured variables, such as item scale, and related latent construct. The structural model allows assessing the magnitude and direction of the relations among the set of measured constructs and is used to verify whether the hypothesized relationships take place in the tested model. In our model, the correlation matrix between factors, reported in Table A2 in Appendix A, was used as an input to estimate the structural coefficients between constructs and latent variables [49]. The SEM models’ goodness of fit was estimated using a chi-square test and recommended incremental goodness-of-fit indices: the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the Tucker–Lewis Index (TLI). The not statistically significant value for the chi-square test indicated that the model fits the data well [50]. CFI and TLI values of approximately 0.90 are widely considered acceptable values of the goodness of fit [51,52]. RMSEA value of 0.05 or less indicates a good fit and values up to 0.08 represent errors that approximate those expected in the population [53,54]. The model was estimated using STATA 14.0 software.

3. Results

The results of testing the conceptual models are presented in Table 3. The models converged well and had satisfactory goodness of fit. In detail, the goodness-of-fit indicators were extremely close to the strictest threshold value of 0.90 for CFI and TLI and equal to the cut-off point of 0.05 for RMSEA. Overall, explained variances were 69.20% and 76.60% for willingness to purchase active and intelligent packaging, respectively.
Results from both models, in the second row of Table 3, showed that individual intention to reduce household FW was a good predictor of the willingness to purchase active and intelligent packaging. Also, we performed the Likelihood Ratio test (LR), which provided us a p-value equal to 0.10, rejecting the null hypothesis that one model performs better than two separate models with the 10% significance level. The p-value of 0.10, at the limit of being statistically significant, may indicate that respondents in our sample do not clearly distinguish the difference between the two packages tested. However, respondents aiming to reduce their wastes at home were more willing to purchase the intelligent technological solution rather than the active one as pointed out from the magnitude of the coefficients in the second row of Table 3. The estimated parameter sizing the association between intention to reduce waste and willingness to purchase intelligent packaging was equal to 0.812 (p < 0.001) and larger than that for the willingness to purchase active packaging that was equal to 0.679 (p < 0.001) to achieve lower household FW. Also, we performed a coefficients equality test assessing whether the parameter 0.812 was statistically different from 0.679. The statistic for the equality test was F (2, 256) = 3.555 with Prob > F 0.03. Thus, we reject the null hypothesis that the two coefficients were statistically equal at 5% of the significance level. Such a result was consistent with the summary statistics reported in Table A2 in Appendix A, second column, reporting the mean value for the intention to buy intelligent packaging (6.29) higher than that for the intention to buy active packaging (5.81). Also, in this case, we performed the paired means t-test to analyze whether the means were statistically different (6.29 vs. 5.81). The p-value for the test was equal to 0.0000401, statistically significant at 0.01% level. Then, we rejected the null hypothesis of means equality.
Concerning the determinants of the intention to reduce the household FW, four out of the seven individual-related variables assessed in our conceptual models, and selected according to the literature, showed a positive and significant effect on the individual’s intention to lower FW at the household level. In both models, attitude towards FW was the strongest predictor of the intention to reduce wastes at home (0.400 and 0.384; p < 0.001). Then, the individual perceived behavioral control was the second most important individual-related factor driving consumers’ intention to lower FW with positive and significant coefficients equal to 0.295 (p < 0.05) and 0.327 (p < 0.01) in both models. The intention to reduce household FW was also determined by the awareness about social and environmental issues related to FW, with magnitude of the coefficients equal to 0.218 (p < 0.01) and 0.239 (p < 0.05). Lastly, concerning the variables related to the household food management, the individual ability to plan food routine was the only individual-related factor that was positively and statistically significantly associated with the individual intention to lower household FW, with coefficients equal to 0.167 (p < 0.001) and 0.175 (p < 0.05) in the two models estimated. Instead, subjective norms, shopping routines, and leftovers reuse routines were not significantly related to the intention to reduce household FW in our sample.

4. Discussions

The present study explored to what extent consumers’ willingness to purchase active and intelligent packaging is associated with their intention to reduce household FW. Results pointed out that consumers are more willing to purchase intelligent packaging rather than active packaging to reduce their wastes generated at home, thanks to the ability of this package to provide real-time use-by or expiration data. Indeed, recent findings suggest that consumers see the package as a tool to protect the food from damage, keeping it safe and with extended shelf life [55]. Also, consistent with previous studies, consumers are more likely to accept intelligent packaging over active packaging since they are often concerned about the toxicity of active substances added to polymer films, as well as scared about the accidental ingestion of active sachets, or whether their content gets disintegrated in handling the product [56,57,58]. Instead, in the case of intelligent packaging, studies found it most likely to be accepted as consumers believe that they have more control over such type of packaging and that it does not interfere with the food product and thus with human health [59]. This finding was also supported by a recent study by Tiekstra et al. (2021), who interviewed 1249 individuals from 16 European countries, showing that active packaging proved more difficult, rather than the intelligent one, to be successful in the market due to lower consumer acceptance about this technology [60]. Besides that, it is worth saying that consumers, on average, have lack of knowledge regarding active and intelligent packaging and are unfamiliar with the technologies used to produce such packages. Indeed, a recent study by O’Callaghan and Kerry (2016), exploring consumer behavior towards emerging food packaging technologies, in a sample of consumers from 33 different countries, found a general lack of consumer knowledge/familiarity regarding active and intelligent packages which led to a low level of acceptance to such packages [59]. This would work as barriers for active and intelligent packaging adoption, which were mitigated after providing general information about the use of these food packaging solutions [59,61,62,63].
Furthermore, in this study, we investigated which factors are able to explain the intention to reduce household FW. Then, we mainly relied on the TPB and we also added several constructs from the literature that appeared relevant in explaining food-related behavior. Overall, our findings showed that the TPB explained very well the consumer’s intention to reduce household FW. Specifically, attitude towards FW was found to be the main predictor of individual intention according to our estimates. This result finds support in the majority of studies on consumer and FW-related behavior. In detail, attitude toward the behavior reflects the personal positive or negative evaluation of performing that specific behavior. According to Stancu et al. (2016) and Visschers et al. (2016), the more negative the attitude toward FW, the stronger the personal intention to reduce the amount of household FW [35,37]. In other words, the more consumers were opposed to wasting food and were concerned about this issue, the more they were willing to lower wastes at home.
In addition, perceived behavioral control emerged in our study as the second most important driver of the intention to reduce household FW. Generally, individuals are more likely to engage in a specific behavior that is considered to be achievable [34]. Thus, consumers may be very willing to waste less food if they believe they are capable of modifying their behavior related to FW. This positive and significant relationship between perceived behavioral control and the behavior in question was revealed by many other studies using the TPB [37,44,64].
However, in our study, subjective norms did not appear significantly related to the consumer’s intention to reduce household FW. Subjective norms concerning the social pressure to engage in a specific behavior were also found to be a weak predictor of individuals’ intentions in other studies [35,37,45,64]. It could mean that what relatives and friends think about the importance of reducing wastes at home is not as strong in predicting the individuals’ intentions as what respondents personally think about this type of behavior [35,37]. Indeed, taking into account the sociodemographic characteristics of our sample (Table 2), respondents have a medium-high education level, meaning that they are intrinsically motivated by the importance of lowering household FW. Then, regardless of what others think about the behavior in question, respondents are personally intentioned to lower the amount of household FW.
Furthermore, results from our study showed that people who are aware of the FW issue are more likely to avoid wasting food. Then, people with high environmental and social consciousness related to FW are more willing to engage in this behavior. This result was also supported by Principato et al. (2015), who, during a study on 233 Italian consumers, found that the greater the level of concern about the FW issue, the greater the intention to reduce waste at the household level [36].
Finally, several constructs such as shopping routines, planning routines, and leftover reuse routines were added to the conceptual model because they are considered significant, based on previous studies present in the literature, in predicting food-related behavior [35,39,40,44]. However, our results showed that only planning routines are relevant in explaining the consumer’s intention to reduce household FW. This result was supported by several studies demonstrating that planning routines like meal planning and making shopping lists play an important role in avoiding unplanned purchases and then minimizing wastes at home [39,65]. Indeed, buying more food than needed increases the probability of food spoilage [40,45,66]. This emphasizes the importance of checking what foods are available in the fridge before going shopping and then planning meals, as well as making a shopping list, in order to avoid household FW.

5. Conclusions

The current work sheds light on the positive relationship existing between consumers’ intention to reduce FW and their willingness to use active and intelligent packaging to achieve this goal. Such innovative packaging solutions, by extending food products’ shelf life (active packages) or making consumers aware that food is nearing the expiry date (intelligent packages), promote food waste reduction at the household level. Also, the individual intention of reducing FW is driven by consumers’ own attitudes to consider generating FW as irresponsible behavior, as well as by the perceived control over their own actions, the level of awareness about the negative impact of wasting food on the environment, the society, and the economy, and lastly, by their own ability to plan meals and to make shopping lists.
These results come with relevant policy and marketing implications. Policymakers and companies may develop informational campaigns to raise the level of consumers’ knowledge about these technological solutions to encourage their acceptance and adoption among consumers. Furthermore, policies such as informational and educational campaigns should also be focused on raising awareness about negative effects of FW among consumers, which may have an important role in supporting intentional and behavior changes (e.g., providing recommendations and practical tips to cut wastes at the household level, for instance, by planning portions/meals and not cooking more than needed). Additionally, policymakers may also promote the use of positive messages to increase individual intentions to lower FW. Such messages should point out other benefits of saving food (e.g., saving money, time, etc.) over messages highlighting the negative social and environmental impacts of FW. This will offer a holistic view of multiple benefits arising from consumers’ behavioral change.
Finally, some potential limitations should be taken into account in assessing our results. First, given the small sample size, caution must be used in assessing our results as they might not be transferable to the Italian population and cannot be generalized in other geographical contexts. Also, in our sample, two out of three respondents have a high level of education, which may significantly affect the consumers’ awareness of FW as well as their attitudes and subjective norms related to individual intention to lower the household FW.
Second, the drivers affecting individual intention to reduce FW may be incomplete as contextual factors such as economic, sociocultural, industrial-productive, and environmental aspects of the country where individuals live also played an important role interacting with their own intention to reduce FW. Another factor that could also be taken into account for further research could be the personal direct or indirect exposure to chronic disease that could affect the individual’s intention to adopt a particular behavior able to minimize risks for human health. Therefore, addressing our research question by using the TPB could be considered as another limitation of the study as it does not take into account environmental or economic factors that may influence the personal intention to perform a behavior, as well as the role of the individuals’ emotions and unconscious stimuli during the decision-making process.
Finally, the study used self-declared intention to reduce household FW without observing a behavioral outcome (e.g., the actual amount of waste generated at home). Thus, future research will be aimed to fill these limitations listed above using a larger and more representative sample of the Italian population, accounting for contextual factors that may play a role in FW generation (e.g., by using recent empirical approaches based on nudging theory) as well as by attempting to use a behavioral outcome to measure wastes generated at home.

Author Contributions

Conceptualization, A.C., R.V. and F.B.; methodology, A.C. and F.B.; formal analysis, A.C. and F.B.; data curation, A.C.; writing—original draft preparation, A.C.; writing—review and editing, A.C. and F.B.; supervision, R.V. and F.B.; project administration, R.V.; funding acquisition, R.V. All authors have read and agreed to the published version of the manuscript.


This research was funded by PON FSE-FESR “Research and Innovation 2014–2020”—Axis I “Human Capital”, Action I.1 “Innovative Doctorates with industrial characterization”, Italian Ministry of University and Research (MUR).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data sharing not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. General information about active and intelligent packaging.
Table A1. General information about active and intelligent packaging.
Active PackagingThe Regulation 450/2009/EC defines active materials as: “materials that are intended to extend the shelf-life of foods and to maintain or improve the condition of packaged food. They are designed to deliberately incorporate components that may release substances into the packaged food or the surrounding environment or absorb some substances from food or the environment”. Therefore, active packaging refers to substances added to polymer films or sachets inside the packaging that can absorb (scavengers) or release (emitters) gaseous matter (e.g., ethylene, oxygen, moisture, carbon dioxide).
Intelligent PackagingThe Regulation 450/2009/EC defines intelligent materials as: “materials which monitor the condition of packaged food or the environment surrounding the food”. Therefore, intelligent packaging provides information to manufacturer, retailer and consumer about food quality and safety based on the ability to test, detect or record external or internal changes in the product’s environment.
Table A2. Descriptive statistics and correlations (n = 260).
Table A2. Descriptive statistics and correlations (n = 260).
1. Intention to reduce household FW4.60.871
2. Attitudes6.720.70.26 **1
3. Subjective norms6.270.950.18 **0.43 **1
4. Perceived behavioral control6.230.940.30 **0.47 **0.35 **1
5. Awareness6.370.860.23 **0.48 **0.34 **0.50 **1
6. Shopping routines3.571.34−0.06−0.00−0.02−0.08−0.081
7. Planning routines5.21.450.14 *0.20 **0.14 *0.20 **0.19 **−0.41
8. Leftovers reuse routines5.631.650.15 **0.28 **0.12 *0.13 *0.14 *−0.13 *0.021
9. Willing to purchase active packaging5.811.380.11 *0.13 * *0.15 *0.09−0.041
10.Willing to purchase intelligent packaging6.291.020.15 **0.24 **0.13 *0.34 **0.20 ** **1
Note: * and ** indicate 5 and 1 per cent significant levels, respectively.


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Figure 1. Determinants of intention to reduce household food waste and willingness to purchase active and intelligent packaging.
Figure 1. Determinants of intention to reduce household food waste and willingness to purchase active and intelligent packaging.
Sustainability 13 04486 g001
Table 1. Description of some active and intelligent packaging technologies.
Table 1. Description of some active and intelligent packaging technologies.
Active Packaging
Antimicrobial packagingAntimicrobial packaging systems have been manufactured in order to regulate the microorganisms that threaten to affect the quality and safety of food packages.[19,27]
Antioxidant packagingAntimicrobial packaging prevents the lipid oxidation that is the cause of food degradation thanks to the incorporation of antioxidant agents in thin polymeric films.[28,29]
Ethylene scavengersA range of different technologies that involve chemical reagents added to polymer films or sachets to absorb ethylene, which is the cause of deterioration of fresh and minimally processed fruits and vegetables.[7,21,29]
Moisture scavengersPads made from super-absorbent polymers, which absorb moisture, maintaining conditions that are less favorable for the growth of microorganisms.[7]
Odor and flavor scavengersActive technology able to protect the specific aroma of foods by removing unwanted odors and flavors.[20,30]
Oxygen scavengersSachets with active substances, generally in powder form, that are able to remove the oxygen from a closed package.[7]
Intelligent Packaging
Freshness indicatorFreshness indicator, which works based on a calorimetric approach, provides information about food quality, showing an intense color change when it detects microbial growth or chemical alteration inside the food product.[19,23]
Gas sensorsGas sensors are intended to detect and indicate the presence of gaseous or volatile compounds (e.g., carbon dioxide, oxygen, volatile amines).[23]
Leak indicatorLeak indicator monitors the integrity of the package to which it is attached.[31]
Temperature indicator (TI)TI informs the user when the product has the correct serving temperature to be drunk or eaten.[24]
Time–temperature indicator (TTI)TTI constantly monitors temperature over time and shows the actual remaining shelf life of the food product to which it is attached.[18,32]
Table 2. Sociodemographics characteristics of respondents (n = 260).
Table 2. Sociodemographics characteristics of respondents (n = 260).
Categorical Variables Sample %
Primary school 0.4
Middle school 0.8
High school 32.3
Bachelor’s degree18.5
Master’s degree23.1
Postgraduate (e.g., PhD, master)25
Not employed/student/housewife46.9
Family monthly income
Up to EUR 1000 10.4
EUR 1001–300046.5
EUR 3001–500018.8
EUR 5001–70005.8
EUR 7001 and over18.5
Continuous variablesMean/SD/Range [min.–max.]
Age35.8/11.7/[20 min.–81 max.]
Household size3.4/1.2/[0 min.–8 max.]
Number of children (under 14 years old)0.4/0.7/[0 min.–4 max.]
Number of employed in family (excluding interviewed)1.3/0.9/[0 min.–5 max.]
Table 3. The structural model of intention to reduce household food waste and willingness to purchase active and intelligent packaging.
Table 3. The structural model of intention to reduce household food waste and willingness to purchase active and intelligent packaging.
ParametersWillingness to Purchase Active PackagingWillingness to Purchase Intelligent Packaging
Intention to reduce household food waste 0.679 *** 0.812 ***
Intention to reduce household food wasteIntention to reduce household food waste
Attitudes 0.400 *** 0.384 ***
Subjective norms0.0180.021
Perceived behavioral control 0.295 ** 0.327 *
Awareness 0.218 * 0.239 **
Shopping routines0.1180.127
Planning routines 0.167 *** 0.175 **
Leftovers reuse routines0.0940.109
Indexes of goodness of fit
Likelihood Ratio χ2 (6)29.7 p-value < 0.00134.7 p-value < 0.001
Note: *, **, and *** indicate 10, 5, and 1 percent significance levels, respectively; Likelihood Ratio test p-value equal to 0.10.
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Cammarelle, A.; Viscecchia, R.; Bimbo, F. Intention to Purchase Active and Intelligent Packaging to Reduce Household Food Waste: Evidence from Italian Consumers. Sustainability 2021, 13, 4486.

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Cammarelle A, Viscecchia R, Bimbo F. Intention to Purchase Active and Intelligent Packaging to Reduce Household Food Waste: Evidence from Italian Consumers. Sustainability. 2021; 13(8):4486.

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Cammarelle, Antonella, Rosaria Viscecchia, and Francesco Bimbo. 2021. "Intention to Purchase Active and Intelligent Packaging to Reduce Household Food Waste: Evidence from Italian Consumers" Sustainability 13, no. 8: 4486.

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