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Millennials’ willingness to pay for green restaurants

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The hospitality industry is currently witnessing an increase in the number of restaurant companies with sustainable business models. This research explores the determinant factors of millennials’ willingness to pay (WTP) by looking at the qualitative decision of whether to pay more and the quantitative decision of how much extra to pay. While literature has investigated the factors that lead people to choose green restaurants, no analysis that simultaneously considers the qualitative and quantitative decisions has been conducted for the millennial generation. This study fills this gap by estimating the Heckit model, which (1) allows us to simultaneously model both decisions and detect their determinants—“green consumerism,” “health consciousness,” “income,” and two psychographics (“green restaurant preference” and “predisposition to make an effort in terms of time and distance”)—and (2) permits the control of sample selections bias, which turns out to be a critical issue in this research.
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MILLENNIALS’ WILLINGNESS TO PAY FOR GREEN
RESTAURANTS
Juan Luis Nicolau
Howard Feiertag Department of Hospitality and Tourism Management
Pamplin College of Business
Virginia Tech
Blacksburg VA 24061
USA
Phone 540-231-8426
e-mail: jnicolau@vt.edu
Mireia Guix
School of Management and Business
Universidad del Rosario
Calle 12C Nº 6-25 - Bogotá D.C.
Colombia
Gilda-María Hernandez-Maskivker
School of Tourism and Hospitality Management SHTI
Ramon Llull University
40-42 Marqués de Mulhacén
Campus ESADE
08034 Barcelona
Spain
Noemí Molenkamp
School of Tourism and Hospitality Management SHTI
Ramon Llull University
40-42 Marqués de Mulhacén
Campus ESADE
08034 Barcelona
Spain
Citation:
Nicolau, J. L., Guix, M., Hernandez-Maskivker, G., & Molenkamp, N. (2020). Millennials’
willingness to pay for green restaurants. International Journal of Hospitality Management, 90,
102601.
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MILLENNIALS’ WILLINGNESS TO PAY FOR GREEN
RESTAURANTS
Abstract
The hospitality industry is currently witnessing an increase in the number of restaurant
companies with sustainable business models. This research explores the determinant factors of
millennials’ willingness to pay (WTP) by looking at the qualitative decision of whether to pay
more and the quantitative decision of how much extra to pay. While literature has investigated
the factors that lead people to choose green restaurants, no analysis that simultaneously
considers the qualitative and quantitative decisions has been conducted for the millennial
generation. This study fills this gap by estimating the Heckit model, which (1) allows us to
simultaneously model both decisions and detect their determinants—“green consumerism,”
“health consciousness,” “income,” and two psychographics (“green restaurant preference” and
“predisposition to make an effort in terms of time and distance”)—and (2) permits the control
of sample selections bias, which turns out to be a critical issue in this research.
Keywords: green restaurants; willingness to pay; green consumerism; health consciousness;
social media; Heckit model.
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MILLENNIALS’ WILLINGNESS TO PAY FOR GREEN
RESTAURANTS
1. Introduction
Green restaurants emerge as a response to the environmental concern in the food-service
industry, wherein consumers and producers have become aware of the effects of food
production on climate change. Green restaurants take an environmentally friendly approach to
their designs, constructions and operations (Lorenzini, 1994), including other practices, such as
water efficiency, recycling, pollution reduction or energy saving (Teng, Wu, & Huang,
2014).The definition of green restaurants, or more broadly speaking, sustainable restaurants is
complex. The Sustainable Restaurant Association defines them as “restaurants managing the
social and environmental impacts of their operations”, which considers three critical elements:
sourcing, society and environment (Higgins-Desbiolles et al., 2017). Note that these authors
point out that “the boundaries between considerations of sustainability and ethics, corporate
social responsibility and “greening” agendas are loose”. As this study focuses on green
consumerism, we use the term green restaurants.
The analysis of green restaurants has sparked interest in the literature (Cantele and
Cassia, 2020; Hwang and Lee, 2019; Kwok and Huang, 2019), and the previous research has
dealt with how consumers perceive restaurants with green attributes and how this scenario
influences their attitudes and behavioral intentions (Dutta, Umashankar, Choi, & Parsa, 2008;
Jeong & Jang, 2010; Kwok, Huang, & Hu, 2016). This research focuses on the millennial
generation, which includes those who were born between 1980 and 2000, reaching young
adulthood in the early 21st century (Strauss & Howe, 1991). Despite millennials having high
pre-disposition to engage in environmentally friendly consumption (McGlone, Spain, &
McGlone, 2011) and having more income than other generations (Farris, Chong, & Danning,
2002), there are still areas of research on this target segment in the context of green restaurants
that merits further analysis. While the behavior of millennials as green consumers have been
analyzed (Naderi & Van Steenburg, 2018), this study fills a gap in the literature as it aims to
detect the determinant factors of millennials’ willingness to pay (WTP) from a twofold
viewpoint: on the one hand, the qualitative decision of whether to pay more is analyzed, and on
the other hand, the quantitative decision of how much extra to pay is examined. Note that the
term “qualitative” is used here from a statistical viewpoint representing a choice situation where
an individual makes a choice between two courses of action, i.e. between two alternatives
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(Train, 1993). This distinction between qualitative and quantitative decisions allows us to
observe the way a specific variable may have an effect on one decision but not on the other or
the different degrees of impacts of one same variable on two different decisions. This twofold
analysis contributes to the literature by looking into several dimensions from different
perspectives—qualitative and quantitative—with the corresponding differentiated effects;
effects that are quantified.
Based on theory of reasoned action (TRA), this study proposes three key dimensions,
namely, “green consumerism,” “health consciousness,” and “social media,” to estimate the
Heckit model, which apart from allowing us to simultaneously model the decision of whether
to pay extra and the decision of how much extra to pay, allows us to control potential sample
selection bias that could exist.
For this purpose, the next section shows the framework of analysis where the link
between TRA and WTP is briefly outlined to move forward with the hypothesis development,
Section 3 presents the research design with the description of sample data and methodology,
Section 4 discusses the results, and Section 5 shows the conclusions with the theoretical and
managerial implications.
2. Framework of Analysis: Theory of Reasoned Action, Theory of Planned Behavior, and
Willingness to Pay
Consumer behavior has been extensively explained through TRA (Fishbein & Ajzen,
1975). TRA links attitudes, subjective norms, and behavioral intentions as the determinants of
consumer behavior. Attitudes are formed by values, which are personal standards that influence
people’s actions (Clawson & Vinson, 1978). Subjective norms are a form of social pressure,
where a person is influenced by their beliefs on how they perceive people close to them expect
him or her to behave (Fishbein & Ajzen, 1975). One main condition of this theory is that the
target behavior is completely under a person’s volitional control.
However, Ajzen (1985) explained that this is not always the case; therefore, he added
the construct “perceived behavioral control” to the TRA, resulting in TPB. Perceived behavioral
control considers variables that are out of an individual’s influence, such as the perceived
difficulty inherent in a specific behavior (Ajzen, 1991). Although TPB has proven its relevance
in some studies of green restaurant visit intention (Ching-Yu Lien, 2012), other studies have
shown that there is no correlation between perceived behavioral control and behavioral
intentions (Kim et al., 2013). In addition, Fishbein and Ajzen (1975, p. 380) stated: “Since much
4
human behavior is under volitional control, most behaviors can be accurately predicted from
an appropriate measure of the individuals’ intention to perform the behavior in question.”
Given that the selection of a green restaurant is assumed to be a behavior that is under
a person’s volitional control, TRA is used instead of TPB (see Figure 1 wherein the concepts
in the dotted square show the relationships analyzed in this article). WTP is used here as a proxy
measure of behavioral intention (Dutta et al., 2008; Kang et al., 2012). WTP can be defined as
“the maximum amount of money that people are willing to pay for products or services”
(Krishna, 1991; Homburg, Koschate, & Hoyer, 2005). Analyzing WTP allows companies to
plan competitive strategies, design personalized offers, and make effective decisions. Some
studies on WTP have gone a step forward and have specifically examined factors that may have
an influence toward it (Hernandez-Maskivker, Nicolau, Ryan, & Valverde, 2019) and the
characteristics of customers who are predisposed to pay a premium price (Jang, Kim, & Bonn,
2011).
The behaviors studied here involve beliefs and attitudes on consumerism and health
consciousness, while the subjective norm is analyzed by the influence of social media on
customers, which are further elaborated in the next sections.
[Insert Figure 1 about here]
2.1. Green consumerism and effect on willingness to pay
A green consumer is one “who takes into account the public consequences of his or her
private consumption or attempts to use his or her purchasing power to bring about social
change” (Webster, 1975, p. 188). Green consumers tend to show an environmental concern,
specified by “the degree to which people are aware of problems regarding the environment and
support efforts to solve them or indicate the willingness to contribute personally to their
solution” (Dunlap & Jones, 2002, p. 482). The consensus is that the green customer segment is
conscious of the environment when making purchase decisions. For instance, studies find that
customers are prepared to pay a premium for green products if they reward firms with those
values (Kang et al., 2012; Tsen et al., 2006). Products and services are generally more expensive
compared with conventional products (Vargas-Hernandez, 2015), and the extra amount a
consumer is willing to pay, as a price premium, can be perceived as a justification for the true
value of the product or service.
Results from recent studies have found that showing a concern about the environment
is the most influential explanatory variable of WTP in green restaurants (Namkung & Jang,
5
2017; Sarmiento & El Hanandeh, 2018; Shin, Im, Jung, & Severt, 2019). For instance,
Namkung and Jang (2014) showed that 68.3% of the respondents showed a predisposition to
pay extra for green products in restaurants. Other studies have found that involvement in social
and environmental practices leads to a high WTP level (Dutta et al., 2008).
Previous research has also examined millennials as green consumers and identified them
to be more conscious of the environment than older segments (Tulgan & Martin, 2001), being
aware of the impact of their choices at home and work toward global warming (Lim, 2017;
McDougle, Greenspan, & Handy, 2011). Thus, they are participating in an active way in the
market place (Lee, 2008) and tend to act pro-environmentally (Naderi & Van Steenburg, 2018).
Studies find that young customers are predisposed to pay more for sustainable brands (Lee,
2008). However, little is known regarding millennials’ most valued attributes of a green
restaurant and their WTP a price premium (see Jang et al., 2011 for the only study).
Accordingly, the following hypothesis is proposed:
H1a. A green consumerism attitude has a positive effect on the willingness to pay a premium
for a green restaurant.
H1b. A green consumerism attitude has a positive effect on the amount of premium for a green
restaurant.
2.2. Health consciousness and effect on willingness to pay
Restaurants tend to focus on observable green practices that can be seen by their
customers and enjoy great economic benefits (Chou et al., 2012), such as offering organic,
vegan, or vegetarian food to show their concern for the environment (Wang et al., 2013) and
appeal to health-conscious consumers. Health-conscious customers have favorable attitudes
toward and great willingness to visit green restaurants. Health consciousness is defined as a
person’s perception of his or her healthy lifestyle (Namkung & Jang, 2014). Health
consciousness increases the willingness to buy organic foods (Tarkiainen & Sundqvist, 2009)
and is also a dimension prioritized in purchasing decisions (Jang et al. 2011). Previous studies
have identified health consciousness as a critical dimension that largely explains people’s
intention to visit a restaurant ( Kim, Park, Kim, & Ryu, 2013; Kiwon Lee, Conklin, Cranage,
& Lee, 2014; Shin et al., 2019). For instance, it was placed as the third attribute after the “taste
and presentation of food” (Namkung & Jang 2007). In addition, younger consumers were found
to more strongly believe that green restaurants are healthier than non-green ones (Schubert,
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Kandampully, Solnet, & Kralj, 2010). Similarly, health-conscious consumers were located in
the highest WTP group for green restaurants (Dutta et al., 2008; Namkung & Jang, 2017), and
a recent study has found health-conscious millennials showing the highest level of WTP for
green restaurants (Jang et al. 2011). Thus, the following hypothesis that millennials’ self-
identification with health is positively related to WTP is proposed. Consequently, we
hypothesized the following:
H2a. Health consciousness has a positive effect on willingness to pay a premium for a green
restaurant
H2b. Health consciousness has a positive effect on the amount of premium for a green
restaurant.
2.3. Social media and effect on willingness to pay
The influence of others may impact a customer’s journey: the search of external
information, the evaluation of alternatives, and then the final service consumption (Engel et al.,
1995; Kotler, 2000; Gitelson & Kerstetter, 1995). For instance, Gruenfeld et al. (1996) and
Vermeir and Verbeke (2006) analyzed how friends and relatives influence travel decision
making. Liu, Wu, and Li (2019) explored how positive experiences shared in social media
trigger millennials’ visit intentions to tourist destinations. In line with Godey et al. (2016),
marketing efforts on social media, such as electronic word of mouth, can influence one’s WTP
an extra price, the antecedent of purchasing behavior.
Millennials make great use of social media channels to share and post, such as pictures
of their gastronomic experiences (Barton et al., 2012). They value recommendations from other
people when selecting a restaurant (Jang et al., 2011). As literature explains, millennials dine
out relatively more compared with any other generation, and a large part of their disposable
income is spent on gastronomy (Apresley, 2010). In previous studies, social influence has been
a relevant determinant factor of young customers’ green purchasing behavior (Lee, 2008).
However, social media can impact consumers differently and their WTP for sustainable
products. For instance, Gracia et al (2012) analyzed gender as a moderating factor. They
explained how social influence and WTP a premium are different for women and men, with
women being more sensitive to social issues, and WTP values are higher for them. More
conspicuous results are the outcomes obtained by the study of D’Acunto et al. (2019) that shows
that consumers rely very little on corporate social responsibility/green elements in online
reviews—in their analysis, these authors find that only between 1.55% and 1.76% of online
7
review content mention corporate social responsibility. Certainly, sustainable consumption is a
complex phenomenon that should be analyzed further considering social influencing factors.
On account of these opposing effects, the following hypothesis is proposed:
H3a. Social media has an influence on the willingness to pay a premium for a green restaurant.
H3b. Social media has an influence on the amount of premium for a green restaurant.
3. Research Design
3.1. Methodology
To analyze the determinant factors of millennials’ WTP for a green restaurant, we
estimate the Heckit model. This model allows us to detect the explanatory variables of WTP by
controlling potential selection bias and simultaneously looking into two decisions, one
qualitative and the other quantitative: (1) the qualitative decision refers to whether the
individual is willing to pay extra for a green restaurant and (2) the quantitative decision reflects
how much more this individual is willing to pay for a green restaurant. As pointed out in the
introduction, the term “qualitative” refers here to a choice situation where people make
decisions between two courses of action, i.e. between two alternatives; alternatives that are
mutually exclusive (selecting one alternative means not selecting the other), and contained in a
set of alternatives that is finite and exhaustive, thus, all potential alternatives are included
(Train, 1993).
Assuming ztk is a set of variables k that determines the decision to pay for a green
restaurant (qualitative decision, whether to pay more), reflected by a latent variable dt*, and
k
are the parameters that show the influence of these variables on this qualitative choice, xts is a
set of variables s that represents the dimensions that determine the extra amount willing to pay
(quantitative decision, how much more to pay), and
s are the parameters that capture the
influence of these variables on this quantitative decision. Moreover, several control variables
(CVht) are included in both equations with parameters λh and δh, reflecting their impacts on the
dependent variables in each equation. Thus, the Heckit model is expressed as follows:
𝑑
=𝛾𝑧
 +𝜆𝐶𝑉

 + 𝑢, (1)
𝐸𝑥𝑡𝑟𝑎=𝛽𝑥
 +𝛿𝐶𝑉

 + 𝜀 observed only if dt*>0. (2)
In these equations, error terms ut and
t follow a bivariate normal distribution, in which
the mean is assumed to be zero and variances are
u and
, respectively, with covariance
u.
8
Dummy variable dt equals one if the latent variable is greater than zero (dt*>0), and zero
otherwise. Accordingly, dt=1 means that an individual wants to pay and dt=0 otherwise.
3.2. Sample
The sampling population of the research are millennials between the age of 18 and 35,
and the data are obtained through the distribution of a questionnaire among the millennial
cohort. This is done partly through Facebook (n = 200) and face to face distribution at the hotel
school in Maastricht (n = 53). The survey is completed by 253 millennials, representing a
sample size similar to other studies in this field (Teng et al., 2014). We use a convenience
sample, with face-to-face contact and online contact, along with snowball sampling. Note that
this study explores the millennials’ general perceptions of green restaurants, thus finding what
proportion of the population gives a specific response to the topic is not the purpose of this
study. In this framework, as self-selection might be an issue, we explicitly deal with it in the
empirical application.
3.3. Variables
This section presents the dependent and independent variables used in the model. Table
1 shows the descriptive statistics for these variables.
[Insert Table 1 about here]
Dependent variables
Qualitative decision: whether to pay more. If an individual is willing to pay more for a
green restaurant, then this variable takes a value of 1 and 0 otherwise. This variable is included
in Eq. (1).
Quantitative decision: how much more to pay. The individuals are asked about their
WTP more for a meal at a green restaurant. This variable is included in Eq. (2).
Independent variables—Factors
A factorial analysis is carried out on the items shown in Table 2, and the three factors
obtained (i.e., green consumerism,” “health consciousness,” and “social media”) are used as
explanatory variables.
[Insert Table 2 about here]
Independent variables—Control variables
Prior researchers have attempted to identify green segment according to age, gender,
9
and income, as those demographics relate to consumers’ perception of green practices of
businesses. However, findings have been inconclusive across these studies (Diamantopoulos et
al., 2003). Therefore, we control for these variables. Specifically, the importance that younger
and older customers attribute to green attributes of restaurants differ as well as their WTP. In
particular, age is found to have a negative effect on participation when it comes to
environmental issues (Barber, Taylor, & Strick, 2010), a positive influence on green restaurant
patronage (Hu, Parsa, & Self, 2010), or no effect on customers’ preferences for green hotels
(Han et al., 2011). Another research has found that younger consumers perceive the reduction
of ecological footprint and the use of organic products to be more critical for green restaurants
(Schubert et al., 2010), and studies not specific to restaurants have identified the millennials
being the greenest group (Barber et al., 2010; Diamantopoulos et al., 2003; Tulgan & Martin,
2001). Certainly, several studies have found young consumers to be willing to pay extra for a
green restaurant (Kwok et al., 2016; Namkung & Jang, 2017; Sarmiento & El Hanandeh, 2018);
however, some other studies have found the opposite results (Dutta et al., 2008). The variable
age is measured through three categories: age 1, between 18—23; age 2, between 24—29; and
age 3, between 30—35. The lower age “age 1” is used as the baseline.
Gender effects on green consumerism have also been empirically examined, with
inconsistent findings across studies. Some studies have found no obtained that gender does not
have any effect on the choice of green restaurants (Hu et al., 2010) or levels of needs for
sustainable food services (Kim, Yoon, & Shin, 2015). Meanwhile, other studies have
discovered that female consumers more strongly believe that green restaurants are healthier
(Schubert et al., 2010; DiPietro & Gregory, 2013). Furthermore, in hospitality, females were
found to show a higher predisposition for eco-friendly hotels (Han et al., 2011) and for paying
extra for green practices than men (Namkung & Jang, 2017) and present higher purchase
intentions and WTP for organic menus (Jeong & Jang, 2010). Sarmiento and El Hanandeh
(2018) found men to be more willing to pay extra for a sustainable restaurant, yet women
willing to pay more indicated a much higher extra fee than men. Gender is measure through a
dummy variable which takes a value of 1 if the respondent is a woman, and 0 if a man.
Income levels have an influence on the intention to visit green restaurants and WTP a
premium. Research has confirmed that income levels are positively correlated with green
consumer behavior and with WTP a premium (Dutta et al., 2008; Mmopelwa, Kgathi, &
Molefhe, 2007; Reynisdottir et al., 2008; Schubert et al., 2010). To measure the individual’s
income a categorical variable is used, in which categories are as follows: Income 1, less than
10
€500 (Income 2, between €201—€1000; Income 3, between €1001—€2000; and Income 4,
higher than €2001. The lower level “Income 1” is used as the baseline.
As for psychographics, it is important to recall that, although the previous socio-
demographic characteristics may be relevant to explain green consumer behavior, Plog (1994)
suggested introducing dimensions, which permit the representation of other internal areas of an
individual. To control an individual’s cognitive structure, we introduce two one-dimensional
psychographics, one measures millennials’ preference toward green restaurants, and the other
reflects the effort that—in time and distance—they are predisposed to make. Regarding the first
one-dimensional psychographic characteristic “green restaurant preference” the respondents
are asked to rate the following statement on a 6-point Likert scale: “I would prefer to select a
green restaurant instead of a non-green restaurant when the quality of the food and the price are
the same”. This variable is included in Eq. (2) only, as this variable is used as the “exclusion
restriction”. For the proper estimation of the Heckit models, the “exclusion restriction” imposes
the condition that it is finding a variable that is significant in Eq. (1) but non-significant in Eq.
(2) is necessary so that it is included in the former, but not in the latter when running the
estimation. As for the second one-dimensional psychographic characteristic “effort to satisfy
the green restaurant preference” the same 6-point liker scale is used to evaluate the statement
“I am willing to make an effort in terms of time and travel distance to select a green restaurant
instead of a non-green restaurant”.
4. Results
Prior to estimating the model, we examine the potential existence of collinearity, and
according to the variance inflation factors, all the parameters are below the recommended value
of 10 (Neter, Wasserman, and Kutner 1989); thus, collinearity does not seem to be an issue.
Furthermore, heteroskesdaticity is tested, and the Breusch–Pagan test rejects homoskedasticity
(F=3.96; p<0.01); thus, White’s heteroscedasticity-consistent standard errors are computed.
Table 3 shows the parameter estimates for the two equations: (1) the qualitative decision of
whether the individual is willing to pay more for a green restaurant and (2) the quantitative
decision of how much this individual is willing to pay for a green restaurant. Before describing
the individual parameters, one should emphasize the results that support the use of the Heckit
model. First, the “exclusion restriction” is warranted as the psychographic variable “green
restaurant preference” is significant in Eq. (1) (
k=0.110; t-statistic=1.828; p-value<0.10) and
non-significant in Eq. (2) (δh=—0.175 t-statistic=0.1821; p-value=0.332). More importantly,
11
we find a significant rho parameter (ρ) when estimating the model. This parameter is relevant
as it is a correlation measure that links Eqs. (1) and (2), supporting the use of the Heckit model.
[Insert Table 1 about here]
As for the individual parameters, we find that “green consumerism” is significant in the
decision of how much more to pay (Eq. (2)) and non-significant in the decision of whether to
pay extra (Eq. (1)). Thus, while being conscious of the environment when making purchase
decisions per se does not have any effect on the decision to pay extra, for those who make the
decision to pay extra, this green consumerism attitude leads them to notably increase the amount
they are willing to pay. Consequently, these results support hypothesis H1b that WTP a
premium for a green restaurant is influenced by the green consumerism attitude in the
quantitative decision; however, this support disappears in the qualitative decision of whether to
pay more, thus, hypothesis H1a cannot be accepted.
“Health consciousness” is significant in both equations; hence, the favorable attitude
toward the green restaurants of health-conscious customers seems to foster their WTP extra.
Therefore, hypotheses H2a and H2b that WTP a premium for a green restaurant is influenced
by health consciousness are supported in the qualitative and quantitative decisions.
“Social media” does not seem to have any effect on either decision; thus, hypotheses
H3a and H3b are not supported. The fact that the factor “social media” is not significant in
either equation is counterintuitive, especially considering how important social media is for
millennials. These results, however, are in line with the conclusions of D’Acunto et al. (2019)
that show that consumers rely very little on corporate social responsibility/green elements in
their online reviews.
At this point, stressing the advantage of the use of the Heckit model to analyze both
decisions is relevant as it allows us to detect any combination of effects. Health consciousness
has a double effect on both decisions, green consumerism has a clear-cut impact on the
quantitative decision, but a null impact on the qualitative decision, and social media has no
effect on either decision. If we compare the two significant variables, i.e., green consumerism
and health consciousness, then we find, for the qualitative decision of whether to pay extra,
significant differences (as expected). Particularly, parameter 0.410 is significantly greater than
parameter 0.174 (Wald test=2.948; p<0.01). Hence, health consciousness is more relevant than
green consumerism when it comes to deciding whether to pay a premium for green restaurants.
Nevertheless, no significant differences emerge between both variables for the quantitative
12
decision of how much extra to pay. Specifically, parameters 0.784 and 0.752 are not
significantly different (Wald test=1.25; p=0.210).
Regarding the control variables, age and gender are not statistically significant, in line
with the results obtained by Han et al. (2011) and Hu et al. (2010); thus, these variables do not
seem to have an effect on either decision. Income is significant in the quantitative decision in
the income category between €1000 and €2000. As for the pychographics, we find that the
green restaurant preference has an effect on the decision of whether to pay extra (recall that this
variable was not included in Eq. (2) because it is used as the exclusion restriction). The “effort
to satisfy the green restaurant preference” is significant in both equations; thus, those willing to
make an extra non-monetary effort (remember that this effort refers to time and distance) also are
willing to pay extra monetary effort.
5. Discussion
Previous research has found young consumers to be willing to wait longer, travel farther,
and pay more for a green restaurant (Kwok et al., 2016). The current study expands such results
in that predisposition to invest more time and travel farther have an impact on the decision of
whether to pay extra and on the extra amount they are willing to pay. Similarly, these results
are consistent with previous findings in that the predictors of the WTP extra are health
consciousness (Namkung & Jang, 2017) and green consumerism (Shin et al., 2019).
Furthermore, this research distinguishes their different influences on the decision of whether to
pay extra and the decision of how much more to pay, providing new insights into the existing
literature. Moreover, “social media” is found to have no significant effects toward green
restaurants. Although these results are in line with the conclusions of D’Acunto et al. (2019), it
is counterintuitive, as millennials make an ample use of social media for posting the pictures of
their restaurant experiences (Barton et al., 2012) and dedicate considerable spending on
gastronomy (Apresley, 2010) and as social media influences green purchasing behavior (Lee,
2008). Nevertheless, this result provides further evidence to previous research that has
confirmed social media to impact differently on young consumers’ WTP a premium (Garcia et
al., 2012).
The empirical application conducted allows us to present the following conclusions:
1) Joint modelization. The analysis of people’s WTP can be broken down into two
stages: deciding on whether to pay more and how much more to pay. Therefore, the examination
13
of how much extra to pay (usually conducted in isolation from any other decisions) should be
modelled jointly along with the decision of whether to pay extra because of the interdependency
between them, as proven by the significant correlation found, and because it helps avoid any
potential sample selection bias.
2) Decision of whether to pay extra. The variables that seem to have an impact on this
decision are “health consciousness” and the psychographic variable that shows a green
restaurant preference and the one that measures the individual’s willingness to make an effort
in terms of time and travel distance to select a green restaurant instead of a non-Green restaurant
(see Figure 2). We can conclude that the more millennials become conscious about their health,
the more they are willing to pay extra. Moreover, while having a preference toward green
restaurants is expected to have an impact on people’s WTP more, the non-monetary effort in
terms of time and distance complements the monetary efforts. In other words, those millennials
willing to invest more time and travel further are also predisposed to spend more in green
restaurants.
[Insert Figure 2 about here]
3) Decision of how much more to pay. The determinant factors that seem to exert an
impact on the decision of how much more to pay for a green restaurant are “green
consumerism”, “health consciousness”, Income 2 (between €1000€2000), and the
psychographic variable that measures the individual’s willingness to make an effort in terms of
time and travel distance to select a green restaurant instead of a non-green restaurant (see Figure
3). We can conclude that green consumerism and health consciousness bring about greater WTP
a higher premium. Moreover, people with monthly income between €1000 and €2000 are more
prepared to pay extra (interestingly, high-income individuals do not seem to follow this same
pattern of higher WTP). Finally, the psychographic variable that reflects the effort millennials
is predisposed to show a positive effect on the extra amount they are willing to pay.
[Insert Figure 3 about here]
6. Conclusions
The current research explores the determinant factors of millennials’ WTP from a
twofold view: the qualitative decision of whether to pay more is analyzed and, on the other
hand, the quantitative decision of how much extra to pay is examined through these variables.
Green consumerism, health consciousness, social media, age, gender, income, and some
psychographics. While the literature has looked into the determinant factors that lead people to
14
choose green restaurants (i.e., Kwok and Huang, 2019), no research has conducted this analysis
for the millennial generation by taking into account both approaches to their decisions:
qualitative vs quantitative. While Jang et al.’s (2011) study looked into the millennial
generation’s behavior intentions to visit green restaurants, our study fills this gap by estimating
the Heckit model, which, apart from allowing us to simultaneously model the decision of
whether to pay extra and the decision of how much extra to pay, permits the control of sample
selection bias. The determinant factors are “green consumerism,” “health consciousness,”
“income,” and two psychographics (“green restaurant preference” and “predisposition to make
an effort in terms of time and distance”). Moreover, simultaneously modelling millennials’
willingness to pay (WTP) from a twofold viewpoint—qualitative and quantitative decisions
contributes to the literature by observing the distinctive effects that a specific variable may have
on two different decisions made by the same individual. For example, green consumerism has
been found to have a significant effect on the decision of the amount of premium but was non-
significant on the decision to pay more, and green restaurant preference shows a reverse effect:
significant in the decision to pay more but non-significant in the decision of how much more.
With regard to theoretical implications, we can stress the fact that analyzing the factors that
determine people’s WTP should be conducted by controlling any potential sample selection
bias. While this statement is clearly applicable to any context in which this bias might exist,
this empirical application has proven that for the specific case of the examination of the
dimensions that explain the premium a millennial is willing to pay, taking this sample selection
bias into account by looking at the decision of whether they are willing to pay the premium is
necessary. As for managerial implications, we can point out that while green consumerism and
health consciousness increase the amount a millennial is willing to pay, when it comes to the
decision of whether to pay more, the emphasis should be put on health consciousness. In other
words, identifying market segments among millennials who are health conscious would allow
green restaurants to focus their advertising campaigns on a group of people who favor their
business. Furthermore, middle-income millennials seem to be the market segment that should
be attracted by green restaurants. Finally, as those millennials with a higher WTP more are
predisposed to make an extra effort in terms of time and distance, green restaurants can go
beyond local people to entice their high-value customers.
The main limitation of this article that restrict generalizability is the geographical area
used; in fact, as future avenues for research, the outcomes obtained in this research should
investigated to find support by other analyses on other regions. Also, the cross-sectional
15
character of the data is another limitation and, consequently, testing the variables used in this
empirical application from a longitudinal perspective would be relevant, so that we could see
how the two-stage choice process evolves. Additionally, as the focus of the analysis is
behavioral intentions, to completely present a full perspective of the stated relationships,
behavioral outcomes could be examined to observe the way behavioral intentions transition to
behavioral actions. Also, as the individuals were asked about a generic green restaurant, the
fact that some variables might be context-dependent, the effect could be qualified and tested in
empirical applications with specific contexts. By way of delimitations, we purposely limited
our analysis to millennials so that the analysis of this underexplored market segment can
unearth hidden relationships.
16
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Figure 1: Conceptual model of millennial visit intention of green restaurants and their WTP a premium.
21
Figure 2: Results of the conceptual model of millennial visit intention of green restaurants
22
Figure 3: Results of the conceptual model of millennial WTP a premium.
23
Table 1. Descriptive statistics
Mean/proportion SD
Dependent variables
Whether to pay more
7.5%
How much more to pay
5.25
Independent variables
key dimensions
Green
consumerism
0
1
Health
consciousness
0
1
Social media
0
1
Independent variables
control variables
Age1 18
23 (baseline)
63
.
6%
Age2 24
29
32
.
0%
Age3 30
35
4
.
3%
Gender (woman=1; man=0)
71
.
1%
Income 1 less than €500 (baseline)
27.7%
Income 2 €501–€1000
43
.
4%
Income 3 €1001–€2000
19
.
3%
Income 4 more than €2001
9
.
5%
Green restaurant
preference
5.33
Effort
3.75
24
Table 2: Factor analysis. Rotated component matrix*
Component
Green
consumerism
Health
consciousness
Social
media
I always prefer an environmentally friendly version of a
product.
0.613 0.106 0.360
I participate in
pro
-
environmentally friendly practices.
0.735
0.137
0.052
I care about protecting the environment.
0.883
0.058
0.125
I consider myself to be an environmentally friendly
consumer.
0.800 0.180 0.060
In my opinion, companies should take measures to protect
the environment.
0.762 −0.010 0.208
I choose food carefully to be healthy.
0.288
0.755
0.229
I exercise on average 3 times a week or more.
0.026
0.794
0.079
I consider myself to be a
health
-
conscious person.
0.172
0.888
0.167
I check social media channels for food pictures.
0.047
0.331
0.647
People whose opinions I value would prefer that I select an
eco
-
friendly restaurant
.
0.192 0.193 0.607
The more I encounter a green restaurant on social media, the
more likely I am to visit that restaurant.
0.166 0.117 0.840
The more often my friends tell me to visit a particular green
restaurant
, the more likely I am to go.
0.235 −0.043 0.780
*Each item was measured on a 1–6 Likert scale.
25
Table 3. Determinant factors of the decisions to pay more and how much more
Decision to pay more Decision of how much more
Parameter SD Parameter SD
K
ey dimensions
Green
consumerism
0.174
0.115
0.784
a
0.178
Health
consciousness
0.410
a
0.132
0.752
a
0.193
Social media
0.040
0.108
0.110
0.167
C
ontrol variables
Age2 24
29
0.241
0.252
0.354
0.397
Age3 30
35
0.554
0.521
0.320
0.914
Gender (woman=1; man=0)
0.143
0.242
0.383
0.387
Income 2 €501- €1000
0.266
0.276
0.474
0.395
Income 3 €1001- €2000
0.278
0.344
1.066
b
0.519
Income 4 more than €2001
0.033
0.401
0.339
0.703
Green restaurant
preference
0.110
c
0.060
-
-
Effort in time and distance
0.217
b
0.098
0.464
a
0.154
Constant
0.089
0.498
2.834
0.703
Rho
0.972
a
0.001
a=p<0.01; b=p<0.05; c=p<0.10
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