Which Consumer Perceptions Should Be Used in Food Waste Reduction Campaigns: Food Security, Food Safety or Environmental Concerns?

Abstract

Consumer food waste linked to restaurants and canteens has been a long-standing serious problem in China, which hungers for an effective solution. Although government and media have launched campaigns against food waste, limited information on consumer perceptions is provided in helping to guide campaign programs. The influence of perception associated with low food waste is lacking, along with targeting campaigns based on these perceptions. This research aims to fill this gap. The purpose of this paper is to identify consumption perceptions about food waste in the Chinese urban restaurant sector, so that they can be used in social marketing to promote food waste reduction behaviors. Employing Probit models using survey data collected, we found that food security and environmental perceptions are associated with low food waste. Campaigns directed toward reducing food waste should target raising awareness of food security alert and environmental concerns that are more generally appealing to altruistic spirits.

1. Introduction

Nearly one out of every three people worldwide did not have adequate access to food in 2020 [1]. However, one third to one half of all food produced ultimately goes to landfills, accounting for 7% of total world greenhouse gas emissions [2]. Food waste is considered a major threat to the environment, economy, social welfare, and sustainable development; thus, serious attention should be paid to reducing food waste [3]. Previous studies have shown that food surplus at the consumer level and consumer behavior are the main causes of food waste in developed countries [4,5,6], whereas food wastes at the post-harvesting and processing stages of the supply chain are a huge challenge in developing countries [7,8,9]. Despite its primary occurrence in developed countries, food waste at the consumer stage is becoming a serious problem in large emerging economies as well, which not only results in environmental concerns but also threatens their still vulnerable food security situations [10,11].

As the largest emerging economy, China’s food waste is evident in its annual restaurant waste of 50 million tons, nearly 10% of annual crop production, which is enough food to feed 200 million people [12]. China’s rapid economic growth has resulted in a sharp increase in restaurant meals [13,14], and restaurant waste is far more severe than waste from eating at home [15]. One unique feature of Chinese restaurant dinning culture is that the host usually orders courses for the entire party to share, rather than having guests order for themselves. Ultimately, much of the food is wasted on the table. However, food waste issue in the catering and restaurant sector is not adequately researched in developing countries [16]. This study investigates restaurant food waste in Chinese cities, based on a field survey consisting of 419 restaurant tables in three cities in China.

A large share of restaurant food waste comes from customer leftovers, highlighting the necessity of addressing restaurant food waste at the consumption stage [17]. In this paper, food waste generally relates to consumer behavior issues, being defined as the edible food that has been disposed of as a result of human action or inaction [18]. Previous research has found that social, economic, and environmental factors can affect food waste [19]. Some studies investigated consumer subjective factors and revealed that their perceptions toward the environment and economy, and their demographic characteristics are correlated with food waste [20,21]. Consumers are often unaware of the quantities of food waste they produce and underestimate the implication of their food waste practice on the environment and economy [9,22,23]. Objective conditions are also studied to show that environmental factors, self-interest, and economic resources are three attitude dimensions of food waste [24], and culture and other relevant factors also affect consumer food waste [20]. For example, an increasing variety of foods, lower food prices, and a decreasing percentage of income spent on food may lead to more food waste [25]. Social context, such as whether someone eats alone or in a group, also affects the likelihood of food being wasted [26]. Leftover food from restaurants may be taken home to consume to avoid waste; however, consumers may refuse to take leftovers home out of embarrassment or other social norms [27,28].

Reducing China’s food waste has increasingly attracted public and government attention [15,29]. In response, both government and non-government organizations have launched media campaigns against food waste, which is a social marketing approach. The International Social Marketing Association defined social marketing as follows: “It seeks to develop and integrate marketing concepts with other approaches to influence behaviour that benefit individuals and communities for the greater social good” [30]. Social marketing promotes information with social value through public communication, changes consumer behavior, and maximizes social benefits. Policymakers in many countries, including the United Kingdom, United States, and Australia, have recommended the use of social marketing to address environmental problems in general [31]. Recent research verified the efficacy of social marketing in food waste reduction at the household or individual levels [30,32]. There is limited social marketing literature specifically applied to food waste, especially in developing countries [31,33,34,35,36,37]. Additionally, the causes of food waste are different in developed versus developing countries [31]. Consumers in developing countries may have different perceptions than those in already developed countries, as the former may emphasize economic factors over environmental factors [38]. Food security concerns may be more critical in developing countries, especially in China. Consumers in developing countries may also have less access to scientific knowledge. Thus, it is critical to evaluate the effectiveness of the information provided in social marketing that targets food waste reduction in developing countries.

Kim et al. (2019) [39] summarized many social marketing studies and emphasized its voluntary and consumer-oriented approach to behavioral change. Our example of social marketing in this study is the Chinese government’s communication campaign targeting food waste reduction. We investigate the effectiveness of the content and the target segmentation of the social marketing. We apply behavioral change, perceptions, and segmentation theory as the marketing principles. Behavioral change refers to whether consumers generate food leftovers and take leftovers home afterwards. Behavioral change is the measurement of social marketing effectiveness. Behavioral science literature indicates that decisions are made based on the psychological process of knowledge, recognition, and perception [40,41,42,43,44]. Consumers’ key perceptions related to food waste reduction are food security, food safety or environmental concerns in our context. Effective social marketing campaigns may focus on addressing consumers’ food safety concerns toward leftover food (self-interest) and/or on raising their environmental and food security awareness (altruistic interest). These are the contents of the social marketing campaign. Restaurant diners are further segmented into groups such as frequent diners, family gatherings, etc. Cognitive behavioral theory (CBT) aims to modify food waste behavior by changing or evoking consumers’ key perceptions. Different groups may respond to the social marketing campaign differently. These are the social marketing targeting segmentation. The objective of this paper is to identify more targeted and more effective campaign materials based on social marketing principles, that is, to promote different scales and different forms of information for consumers with different consumption perceptions in different cities (topline, second line, and third line cities) to reduce food waste in the Chinese urban restaurant sector.

2. Materials and Methods

2.1. Sampling Method

We conducted an empirical study linking alternative consumer perceptions with food waste levels in restaurants. An in-person survey was conducted in the fall of 2016 in Beijing, Hangzhou, and Qinhuangda; these are topline, second line, and third line cities in China, respectively. Beijing and Qinhuangdao are located in northern China, while Hangzhou in the south. The food service sector is ranked from most to least active from Hangzhou, Beijing, to Qinghuangdao by China City Food Service Ranking (http://tieba.baidu.com/photo/p?kw=%E4%B8%AD%E5%8D%8E%E5%9F%8E%E5%B8%82&ie=utf-8&flux=1&tid=7537693170&pic_id=7cbece95d143ad4b1d35422bc7025aafa50f064b&pn=1&fp=2&see_lz=1, accessed on 31 May 2021). About 10 restaurants were randomly selected in each city; 10 to 15 tables in each restaurant were randomly selected, and 1 dine-in customer acting as the host or cohost at each table was recruited. We gathered 419 completed surveys in all, collected at lunch and dinner times. A small monetary gift was provided for each completed survey to improve the response rate. While waiting between ordering and the meal’s arrival, restaurant customers were asked face to face about three perceptions (food safety, environment, and food security concerns) along with their socioeconomic demographics and dining experience (

Table 1. Perception survey questions and variable explanation.

Perception	Survey Question	Variable	Description
Food safety	Do you think eating leftover food is harmful to health?	Harm	$\mathrm{Harm}=\{\begin{array}{l}1,\mathrm{if}\mathrm{yes}\\ 0, \mathrm{otherwise}\end{array}$
Environmental	Which do you think is more important, environmental impact or economic growth?	Environ	$\mathrm{Environ}=\{\begin{array}{ll}1,& \mathrm{if\; environmental}\\ 0,& \mathrm{otherwise}\end{array}$
Food Security	How much do you think was China’s grain import ratio (trade deficit ratio) last year?	Ratio_import	Numerical value (percentage)

), using a pen-and-paper personal interview.

At the meal’s end, we observed if the participating customers had uneaten food (Leftovers) and if they did, whether they took it home (Take-home). Leftovers are measured as the portion of edible food people leave on their plates after eating, excluding seasonings and food scraps (bones, shells, etc.). Our trained enumerators roughly estimated the proportion of leftovers on the plate after the customer had finished their meal by visualizing the volume. If the volume of leftovers was less than 5% of the plate, it was considered that there was no food left over. We also recorded their itemized receipts.

Table 2. Summary statistics.

Variable	Statistic
Perception
Ratio_import	Mean 26, Std. Dev. 0.95
Harm	No harm 59%, Harm 41%
Environ	Economy 24%, Environment 76%
Socioeconomic
Age (years)	Mean 36, Std. Dev. 10.02
Male	Male 53%, Female 47%
IncL (under 100 thousand yuan)	38%
IncM (100–500 thousand yuan)	57%
IncH (over 500 thousand yuan)	5%
Segmentation
Often (eat out at least once a week)	Yes 61%, No 39%
Num_gathering (party size)	Mean 3, Std. Dev. 0.09
First_time (new at this restaurant)	Yes 59%, No 41%
Dinner (dinner time)	Dinner 54%, Lunch 46%
Familygat (for family gathering)	Yes 4%, No 96%
Coupon (use coupon)	Yes 24%, No 76%
Rest_level (average per person cost)	Mean 54, Std. Dev. 19.73
Weekend(dining on weekends)	Yes 26%, No 74%
City
Beijing	27%
Hangzhou	37%
Qinhuangdao	36%

lists the summary statistics. Overall, consumers were found to believe that 26% of food in China is imported, which is much higher than the actual 2016 ratio of approximately 5% [45]. This may indicate that in general, Chinese people have some food security concerns. Approximately 41% of consumers were found to have food safety concerns associated with eating leftover food. This is consistent with Chinese food culture where freshness is preferred; grocery shopping is often done daily, as is the preparation of each meal. Approximately 24% believe the environment is a secondary factor to be considered after economic growth.

On average, the sample population was 36 years old, with an age range of 30 to 50, and 47% female. Compared to urban demographics in China, which has an average age of 36 and is 47% female, this sample was representative [46]. Consumers were categorized into three family income groups: the low-income group as a base, IncL, for those with annual income below 100 thousand yuan, accounting for 38% of the sample; the intermediate income group, IncM, for those whose annual income falls between 100 and 500 thousand yuan, accounting for 57%; and the high income group, IncH, for the remaining 5% whose annual family income is above 500 thousand yuan.

For consumer segmentation variables, Often is a dummy variable with 1 indicating that the same party eats together at least once per week and 0 otherwise. Num_gathering is the number of diners in a party. First_time is an indicator variable depicting whether it is the consumers’ first time at this restaurant. We mainly surveyed consumers during lunch and dinner hours (Dinner). In our survey, 54% of consumers were having dinner and 46% lunch. Familygat is also a dummy variable with 1 indicating the party is a family gathering and 0 otherwise. Coupon is a dummy variable with 1 indicating consumers used a coupon for the meal and 0 otherwise. Rest_level refers to expensiveness of a restaurant, measured by the average meal cost per person. Weekend is a dummy variable with 1 indicating consumers were dining on a weekend day and 0 otherwise.

Table 3. Observations regarding having leftovers and take-home (419 observations).

No, without leftovers		Yes, with leftovers
Number	Percent	Number		Percent
43	10	376		90
		Take home	No take home	Take home	No take home
		197	179	52	48

lists the frequency of having leftovers, and if there were leftovers (Leftovers), the frequency of taking them home (Take-home). Approximately 10% of the respondents had no leftovers. Out of the remaining respondents who did have leftovers, 52% took them home.

2.2. Empirical model

To determine the influence of consumer perceptions on food waste in terms of Leftovers and Take_home behaviors, consider the following Heckman two-step Probit model with stage II as

$$Prob\left(y_j=1\right)=Prob(x_j\beta +u_{1j}>0)$$

where $y_j$ is the observed behavior of consumer j taking leftovers home and $x_j$ is the associated vector of all explanatory variables. The stage II model is based on whether leftovers were taken home, given the selection leftovers equation at stage I. This requires first testing whether the perceptions tend to influence Leftovers and Take-home, using the stage I selection equation as

$$Prob\left(w_j=1\right)=Prob\left(z_j\gamma +u_{2j}>0\right)$$

where $w_j$ is the observed behavior of consumer j having leftovers and $z_j$ is the associated vector of explanatory variables. Both $u_1$ and $u_2$ follow standard normal distributions with a correlation ρ. If ρ is not significantly different than zero, then the two equations are independent, and the two independent Probit models suffice:

$$Take\_home=f\left(Harm,Environ,Ratio\_import,othercontrolvariables\right)$$

where Harm, Environ, and Ratio_import denote respondents stating food safety concerns, environmental, and food security perceptions, respectively. In cases where the missing take-home leftovers are not random, a Heckman Probit selection model is employed:

$$Leftovers=f\left(Harm, Environ, Ratio\_import, Rest\_level,other control variables\right)$$

For consistency with the Heckman Probit selection model, the selection model contains an additional variable. Following Xu et al. [14], we assumed that the restaurant level variable (Rest_level), measured by the average individual cost of a restaurant, affects Leftovers but not Take-home.

There are three hypotheses about the direction of consumer perception impacts:

Hypothesis 1 (H1).

Food security perception (Ratio_import) will reduce food waste, that is, consumers will increase the possibility of packing leftovers when they believe that their country’s food imports are relatively large.

Hypothesis 2 (H2)

. When the environment is more important than the economy (Environ), consumers reduce food waste.

Hypothesis 3 (H3).

Food safety perception (Harm) will reduce the possibility of consumers packing leftovers. If consumers think that leftovers are harmful to their health, they will not take leftovers home.

There are hypotheses concerning the directions of the exogenous variable impacts. Relationships within a dinner party may play a role in explaining food leftovers either way, because family (Familygat) dinners relative to business dinners may have fewer leftovers as they are more familiar with how much they can eat, but they may also intentionally order more to take home. In both the Probit and selection equations, Often may have a negative impact on the leftover equation but a positive impact on the take-home equation. If the party dines often together, then the person who places the orders will have a better sense of what and how much to order. They also feel more comfortable with taking home leftovers. Eating at a restaurant for the first time (First-time) may suggest limited knowledge about the portion size of each dish and may lead to food waste. When considering the average individual cost of a restaurant (Rest_level), it is expected that less food will be ordered at expensive restaurants, resulting in less food waste, and higher likelihood of taking home leftovers. Family income (IncL, IncM, and IncH) may have a positive association with the ability to afford ordering and wasting more food. The size of the party (Num_gathering) can go both ways for the equations. A larger party may make it more difficult to order the right amount, resulting in more leftovers, but a smaller party may also result in more leftovers given the desire for variety. In addition, the time of the meals—dinner (dinner) or lunch—may have an impact on the amount of food left over and whether or not it is taken home. It is less feasible to take leftover food back to work after lunch than home after dinner. However, this is not an absolute, because another interesting aspect of Chinese meal culture is that the most important meal is in the middle of the day, and the restaurant menu is the same for lunch and dinner.

Table 4. Variable descriptions and hypothesized signs.

Variable	Hypothesized Sign
	Leftovers	Take-home
Perception
Ratio_import	+	+
Environ	−	+
Harm	−	−
Socioeconomic
Age (years)	?	?
Male	?	?
Income	+	?
Segmentation
Often (eat out at least once a week)	−	+
Num_gathering (party size)	?	?
First_time (new at this restaurant)	+	?
Dinner (dinner time)	?	?
Familygat (for family gathering)	−	+
Coupon (use coupon)	+	?
Rest_level (average per person cost)	−	+
Weekend(dining on weekends)	?	?
City
Beijing	?	?
Hangzhou	?	?
Qinhuangdao	?	?

lists the hypothetical symbols of the variables.

3. Results

The results of the Heckman Probit model are shown in

Table 5. Heckman Probit regression and independent Probit regression results.

	Heckman Probit Results		Independent Probit Regression Results
	Coefficient Estimates		Coefficient Estimates		Marginal Effects b
Variables	Leftovers	Take-home	Leftovers	Take-home	Leftovers	Take-home
Beijing	0.517 **	0.647 **	0.542 **	0.677 ***	0.082 **	0.224 ***
	(0.278) a	(0.253)	(0.286)	(0.188)	(0.043)	(0.063)
Hangzhou	1.733 ***	1.004 ***	1.724 ***	1.235 ***	0.260 ***	0.371 ***
	(0.398)	(0.374)	(0.396)	(0.176)	(0.060)	(0.053)
Harm	−0.172	−0.170	−0.164	−0.179	−0.025	−0.060
	(0.204)	(0.152)	(0.203)	(0.141)	(0.030)	(0.050)
Ratio_import	0.003	0.009 **	0.004	0.008 **	0.001	0.003 **
	(0.005)	(0.004)	(0.005)	(0.004)	(0.001)	(0.001)
Environ	−0.145	0.484 ***	−0.135	0.419 **	−0.020	0.165 ***
	(0.229)	(0.170)	(0.228)	(0.163)	(0.034)	(0.056)
Age	0.010	0.003	0.008	0.005	0.001	0.001
	(0.013)	(0.007)	(0.011)	(0.007)	(0.002)	(0.003)
Male	−0.141	−0.162	−0.160	−0.159	−0.024	−0.057
	(0.218)	(0.152)	(0.206)	(0.140)	(0.031)	(0.049)
Often	−0.156	0.265 *	−0.170	0.211	−0.026	0.086 *
	(0.203)	(0.149)	(0.200)	(0.143)	(0.029)	(0.049)
Num_gathering	−0.055	0.049	−0.051	0.024	−0.008	0.015
	(0.049)	(0.044)	(0.047)	(0.039)	(0.007)	(0.015)
First_time	0.077	0.340 **	0.099	0.350 **	0.015	0.117 **
	(0.223)	(0.152)	(0.211)	(0.142)	(0.028)	(0.048)
IncH	0.171	−0.246	0.127	−0.168	0.019	−0.085
	(0.611)	(0.334)	(0.602)	(0.327)	(0.092)	(0.120)
IncM	−0.121	−0.052	−0.108	−0.076	−0.017	−0.020
	(0.208)	(0.158)	(0.204)	(0.148)	(0.031)	(0.053)
Dinner	−0.222	0.070	−0.258	−0.038	−0.038	0.016
	(0.239)	(0.157)	(0.211)	(0.140)	(0.030)	(0.048)
Familygat	0.435	0.230	0.428	0.354	0.064	0.095
	(0.568)	(0.380)	(0.564)	(0.348)	(0.082)	(0.113)
Coupon	0.115	0.268	0.135	0.261	0.020	0.096 *
	(0.226)	(0.180)	(0.218)	(0.166)	(0.031)	(0.054)
Weekend	0.090	−0.091	0.026	−0.073	0.004	−0.024
	(0.300)	(0.163)	(0.228)	(0.156)	(0.034)	(0.051)
Rest_level	−0.011 *	−	−0.010 *	−	−0.002 *	−
	(0.006)	−	(0.006)	−	(0.001)	−
Constant	1.563 **	−1.484 ***	1.638 ***	−1.579 ***	−	−
	(0.621)	(0.493)	(0.585)	(0.374)	−	−
Ρ	−0.074		−	−	−	−
ath ρ	−0.074		−	−	−	−
Wald test	0.010		−	−	−	−
Wald Chi2	−		38.37 **	60.36 ***	−	−
Number of obs.	415		415	372

^a Standard errors are in the parentheses with *, **, and ***denoting 10%, 5%, and 1% level of significance, respectively. ^b For dummy variables, the marginal effect refers to the probability change resulting from the change of the variable value from 0 to 1.

. The maximum log likelihood estimation is used, which cannot directly estimate ρ. Instead, ath ρ is estimated as $ath \mathsf{\rho }=\frac{1}{2}\ln \left(\frac{1+\mathsf{\rho }}{1-\mathsf{\rho }}\right)$, and ρ is zero only if ath ρ is zero. The ath ρ is insignificant at the 10% level, so the Wald test fails to reject the independence of equations in the Heckman model. Based on this test, the independent Probit regression results are then also listed. The Wald Chi-square tests of the two independent Probit regressions indicate significant explanatory variables at the 5% level. For interpretation, the marginal effects are calculated based on the independent Probit model estimates.

4. Discussion

From these marginal-effects results, the coefficients of the perception variables Ratio_import and Environ are significantly positive at the 5% and 1% level, respectively. This indicates that environmental and food security perceptions are associated with a higher likelihood of taking food home, which leads to less food waste. Specifically, respondents who indicate their preference for the environment over the economy have a 17.6% higher likelihood to take home their leftovers than those who do not. A 1% increase in the perceived Chinese food import portion will increase the take-home leftover food probability by 0.3%. In contrast, food safety concerns (Harm) over leftovers are not significant at the 10% level, which suggests no linkage between food safety concerns and food waste behavior. The results indicate that concerns regarding food security and the environment are the dominating factors. Environmental and food security concerns (Environ and Ratio_import) are possibly based on altruistic preferences, which change consumers’ behavior toward less food waste. In contrast, food safety concerns (Harm), which appeal to consumers’ self-interest, have limited, if any, influence. Thus, policymakers addressing food waste issues may want to design campaigns aimed at arousing altruistic attitudes, such as environmental or food security concerns (Environ or Ratio_import), when eating out.

Furthermore, results indicate that food security (Ratio_import) and environmental concerns (Environ) only have significant impacts on taking home leftovers, but they do not have significant impacts on whether or not consumers have leftovers. Having leftovers from over-ordering or under-eating is influenced by more uncertain factors than the decision of taking food home. For example, a restaurant’s portion size and food taste, along with consumers’ appetite, can all affect the leftovers result. In contrast, ordering an excess of food is for instant satisfaction, while taking home leftovers is more of a corrective measure. The results imply that heightening altruistic spirits has no significant impact on instant satisfaction, while it does have some impact on ex post remedy.

For the control variables, consumers’ age (Age), gender (Male), family income (IncH and IncM), and the number of diners (Num_gathering) do not have significant effects on either equation. The results of leftovers and taking them home do not appear to be sensitive to age, gender, income, or party size factors. This is somewhat different than the findings of previous studies, where Xu et al. (2020) found that high-income consumers are more prone to food waste [14]. Due to the different regions of the survey respondents, the results of income and food waste will be biased. In addition, the purpose and type of parties, i.e., Dinner and Familygat, also have no significant effects on either equation. Consumers who eat out frequently (Often) tend to take home leftovers, but there is no significant effect on Leftovers. Taking leftovers home might be an ex post remedy to avoid wasting food for consumers who eat out frequently. Conversely, consumers who eat out less may see no need to take home leftovers. Further, First_time and Coupon had no significant effect on leftovers and packing. The per capita cost of a restaurant (Rest_level) has a negative effect on Leftovers, demonstrating that consumers tend to have fewer leftovers when they are dining at high-end restaurants. This economic incentive provides some support for food waste tax proposals raised by Katare et al. [5], although the focus of this study is on food waste reduction campaigns, rather than other government instruments. There also exist some regional differences in food culture in China [47]; Qinhuangdao has a lower likelihood of leftovers as well as take-home than Beijing, followed by Hangzhou. Southern China is historically more developed than its northern part, with the exception being Beijing, the capital city, which became the only outlier in recent years [48]. Food culture is rooted deep in history and less influenced by the current economic situation. This is also consistent with the insignificant income variable.

Although this study provides information on consumer perceptions in developing countries, there may be some possible limitations. For example, the dependent variable of leftovers is a dummy variable, and it does not reflect the level of leftovers. This is because our estimated leftover volume is not accurate enough. Another limitation is that the sample size of this survey is not very large due to our budget constraints.

5. Conclusions

Food waste has been playing an increasingly important role in both food security and the environment [49]. As an early attempt in providing empirical evidence for how consumer perceptions impact food waste, this research sheds light on how to develop effective educational campaigns for reducing China’s food waste. Although the current literature shows that food consumption attitudes [41], intention [50], and subjective norms lead to food waste behavior, there is insufficient literature focusing on the perception of food waste behavior in a developing country. This makes it difficult to develop constructive guidance on launching an efficient campaign against food waste in developing countries.

Based on the empirical results, we found that over-ordering and the resulting leftover food wasted are affected by the price level and dining culture in different cities, but not by consumers’ age or income, mealtime (lunch or dinner), or dining occasion (business or family meals). Specifically, the more expensive the restaurant prices in Hangzhou and Beijing were, the more the food waste was. This reveals that food waste reduction campaigns can target a wide range of consumers and situations. From south to north, Hangzhou has the highest leftover rate and Qinghuangdao the lowest. Higher prices discourage over-ordering in the restaurant, as commonly expected.

Employing Probit models, the results showed that environmental and food security perceptions are associated with an increased likelihood of taking restaurant leftovers home, while food safety concerns are not. This implies that environmental and food security concerns motivate consumers’ altruistic behavior. In contrast, addressing food safety concerns may have limited if any influence and only appeal to consumers’ self-interest.

For future policy guidance, the design of social marketing campaigns should consider targeting food security and environmental concerns, which generally appeal to consumers’ altruistic attitudes. Additionally, national campaigns can be focused more on the cities where food service sector is more developed and dining out culture is stronger.