Received: 14 June 2018 | Revised: 6 December 2018 | Accepted: 17 February 2019

DOI: 10.1002/mar.21202

R E S EARCH AR T I C L E

Assessing experiential augmentation of the environment inthevaluationofwine: Evidence fromaneconomic experimentin Mt. Etna, Italy

Gioacchino Pappalardo1 | Roberta Selvaggi1 | Biagio Pecorino1 | Ji Yong Lee2 |Rodolfo M. Nayga2

1Department of Agriculture, Food and

Environment (Di3A), University of Catania,

Catania, Italy

2Department of Agricultural Economics and

Agribusiness, University of Arkansas,

Fayetteville, Arkansas

Correspondence

Gioacchino Pappalardo, Department of

Agriculture, Food and Environment (Di3A),

University of Catania, Via Santa Sofia 98‐100,95123 Catania, Italy.

Email: gioacchino.pappalardo@unict.it

Abstract

In this study, we conducted an experimental auction to determine the impacts that

“experiential augmentation,” a phenomenon in which a physical location impacts

decision‐making, has on consumers’ willingness to pay (WTP) for wine. The

experiment elicited subjects’ valuation under experiential augmentation conditions

for three types of wine grown in the Mt. Etna area in Sicily, Italy. Our findings indicate

that experiential augmentation increased consumers’ WTP for wine. Our results

suggest that experiential marketing practices explicitly related to the environment

where the wine is produced and consumed can lead to increased valuation for wine.

K E YWORD S

environment, experiential augmentation, experiential marketing, wine, WTP

1 | INTRODUCTION

Experiential marketing is a marketing strategy in which customers

recognize and purchase goods or services of a company or a brand

after they amass experiences from attending activities and perceiving

some stimuli (Pine & Gilmore, 1998, 1999; B. H. Schmitt, 1999; B.

Schmitt, 2011). Specifically, stimuli arising from experience can

influence an individual’s emotional state, which, in turn, affects

consumer behaviors (Mehrabian & Russell, 1974). The concept of

experiential marketing is critical for businesses, especially in the

hospitality and tourism industry, and has been applied to many areas,

such as retailing, branding, and event marketing (Jiménez & Voss,

2014; Tynan & McKechnie, 2009; Williams, 2006; Y. H. Yuan &

Wu, 2008).

In the wine sector, experiential marketing is becoming increas-

ingly important since wine success hinges not only on its intrinsic

attributes, such as organoleptic characteristics, but also through

“experiential” attributes that provide hedonic or symbolic values

associated with senses, pleasures, feelings, and emotions (Alebaki,

Menexes, & Koutsouris, 2015; Barrena & Sanchez, 2009; Dawson,

Holmes, Jacobs, & Wade, 2011; Molina, Gómez, González‐Díaz, &

Esteban, 2015). Previous studies have shown that experiential

attributes can be related to wine itself, such as packaging or brand

(Charters & Pettigrew, 2006; Hollebeek, Jaeger, Brodie, & Balemi,

2007; Menival, Fountain, & Charters, 2016; Mora & Moscarola, 2010;

Mueller, Osidacz, Francis, & Lockshin, 2010; Pomarici, Lerro,

Chrysochou, Vecchio, & Krystallis, 2017; Quadri‐Felitti & Fiore,

2016), as well as to the environment where wine is produced or

consumed, for example, the ambience of the winery or wine region

(Alebaki et al., 2015; Bruwer & Alant, 2009; Buccoliero, Bellio, &

Solinas, 2017; Cohen & Ben‐Nun, 2009; Rickard, McCluskey, &

Patterson, 2015).

The experiential attributes related to the environment are

important factors at the stage upon which experience is enacted

and can thus be expected to influence consumers’ decision‐making

(Candi, Beltagui, & Riedel, 2013; Candi, Jae, Makarem, & Mohan,

2017; Creusen, Gemser, & Candi, 2018; Miniero, Rurale, & Addis,

2014). For example, the environment can elicit emotions among

individuals, which can a complex state of feelings that translate into

physical and psychological changes that could then influence thinking

and behavior (Bagozzi, Gopinath, & Nyer, 1999; Spinelli, 2017).

Emotions play an important role in hedonic consumption; that is,

Psychol. Mark. 2019;36:642–654.wileyonlinelibrary.com/journal/mar642 | © 2019 Wiley Periodicals, Inc.

those aspects of consumer behavior that relate to the multisensory,

fantasy, and emotive aspects of individual’s experience with products

(Alba & Williams, 2013; Hirschman & Holbrook, 1982; Holbrook &

Hirschman, 1982). Moreover, emotions are important for marketing

organizations as they help understand consumer behavior. However,

understanding the sources of emotions, the manner in which

consumers seek them, and the ways in which consumers might alter

their hedonic consumption decisions to maximize pleasure and

happiness remain relatively unexplored in the literature.

Past studies have investigated the effect of emotions related to

the environment on food consumption. For example, Kotler (1973)

identified that the “atmosphere” of the location where consumption

takes place is of great importance in eliciting relevant emotions

during food consumption. Mehrabian and Russell’s (1974) framework

specifies that individuals react to their environment along at least

three dimensions: pleasure, arousal, and dominance. However, past

studies that examined the emotions aroused by the environment

have mainly focused on the effects of emotions at the time of

consumption (e.g., Mishra & Bakshi, 2016). To our knowledge, no

study has examined whether the emotional state aroused by the

environment in which consumers evaluate a food can persist even

outside these environments; that is, after the consumer has lived the

experience of visiting a place capable of arousing emotions. This is an

important topic for companies and marketers given that the adoption

of experiential practices related to the environment can lead to an

increase in consumer demand that could persist over time.

To assess the effects of the environment on the evaluation that

consumers assign to food, we conducted an exploratory study on the

effects of the environment in the evaluation of wine. In the wine

sector, previous studies have shown that the environment in which

wine is consumed can stimulate behavioral responses among

consumers that can lead to wine purchases (Platania, Platania, &

Santisi, 2016, Sturiale & Scuderi, 2017). For example, stimuli from the

place of consumption can satisfy the sensory and psychological

aspects connected to the consumption of wine (Orth & Bourrain,

2005; Platania, Rapisarda, & Rizzo, 2016). Other environmental

attributes that influence the consumption of wine can be related to

the region of origin (Alant & Bruwer, 2004; Lange, Martin, Chabanet,

Combris, & Issanchou, 2002; Vecchio, 2013; J. Yuan, Cai, Morisson, &

Linton, 2005) and cultural attractions of the production site that are

capable of developing emotions in highly motivated consumers who

come from areas far from the production area (Getz & Brown, 2006).

Understanding how the environment affects wine valuation and

consumption remains an unresolved issue. Specifically, an interesting

issue not yet fully explored that we analyzed in our study relates to

the concept of experiential augmentation (Candi et al., 2013; Creusen

et al., 2018; Voss, Roth, & Chase, 2008), that is, the efforts made to

enhance hedonic and symbolic value of a product (Chitturi,

Raghunathan & Mahajan, 2007; Candi et al., 2017; Reimann,

Zaichkowsky, Neuhaus, Bender, & Weber, 2010; Teng, Tseng, &

Wu, 2007). In the wine sector, experiential augmentation can involve

a range of components related to the products themselves (e.g.,

sensory characteristics, product packaging, and brand) or to the

environment in which consumers evaluate wine (e.g., the ambiance of

a winery, wine store, and wine region). Hence, experiential

augmentation in the wine sector could influence consumers’

decision‐making with potential implications for the valuation of wine.

The existing literature on experiential marketing in the wine

sector does not clearly distinguish between experiential augmenta-

tion related to the wine itself and experiential augmentation applied

to the environment in which wine is evaluated. Moreover, while a

substantial body of research has analyzed factors affecting the

purchase of experiential services such as consumers’ emotions,

sociodemographic and psychographic characteristics of winery

visitors, and marketing practices (e.g., Alant & Bruwer, 2004; Galati,

Crescimanno, Tinervia, & Fagnani, 2017; Galloway, Mitchell, Getz,

Crouch, & Ong, 2008; Getz & Brown, 2006; J. Yuan et al., 2005), no

other known study has examined the effects of experiential

augmentation explicitly applied to the environment in which the

wine is evaluated on consumers’ valuation for wine. Understanding

how the environmental context influences consumers’ evaluation of

wine could have important implications for marketing and the

competitiveness of the wine sector in a wine region. Since

experiential augmentation of the environment can provide wine

consumers with emotions and feelings that can positively affect the

consumption of wine, it could be used as an important and distinct

differentiation tool by wine producers and marketers. Such efforts

would benefit the wine industry in the region as well as the local

communities within the region.

To fill this gap, this study assesses whether the experiential

augmentation explicitly applied to the environment in which wine is

evaluated affects consumers’ willingness to pay (WTP) for wine and

whether the effect of the environment persists among consumers

over time and outside the experiential environment upon which the

consumers originally evaluated the product. For this purpose, we

conducted a nonhypothetical experimental auction in Italy to

estimate the experiential effects of the environment on consumers’

WTP for wine. The experiment involved recruiting people to evaluate

in blind conditions three types of wine under conditions of

experiential augmentation applied to the environment of Mt. Etna

in Sicily, which is the largest volcano in Europe and one of the most

well‐known wine areas in Italy (Caniglia, D’Amico, & Peri, 2008;

Tudisca, Di Trapani, Donia, Sgroi, & Testa, 2014).

2 | BACKGROUND INFORMATION ONMt. ETNA

Mt. Etna is the largest volcano in Europe. Every year, Mt. Etna

attracts millions of visitors from all over the world, not just for its

natural beauty but also for the richness of cultural heritage resulting

from a centuries‐old interaction between local communities and the

surrounding environment. Currently, Mt. Etna is protected by a vast

natural park, which was established in 1987. Moreover, in June 2013,

Mt. Etna became part of the UNESCO World Heritage List which

demonstrates the environmental value of the volcano’s area.

PAPPALARDO ET AL. | 643

Over the centuries, a rich agriculture has developed around the

volcano, mainly consisting of fruit, olive, and wine production. Most

of these productions are recognized by the European Union with the

“Protected Designation of Origin” (PDO) label, which promotes and

protects names of quality agricultural and food products. Over the

centuries, anthropic activities have left a profound mark on the

agricultural landscape, thanks to terracing works, warehouses,

cellars, and many other rural architectural artifacts.

Mt. Etna is one of the oldest territories of Sicilian agricultural

civilization, with the first evidence of agricultural communities dating

back to the Neolithic period. The first historical record of wine

production around Mt. Etna dates back to the third century BC when

Theocritus speaks of the great diffusion of the vineyard on the slopes

of Etna. Etna viticulture over the centuries has always played a very

important role for the territory from an economic point of view

(Selvaggi, Verduci, & Pecorino, 2018). Currently, with >700 vine-

yards, a wine surface around 1,400 hectares, and >50 cellars, the

wine obtained around Mt. Etna is recognized worldwide as a high‐quality wine (Tudisca et al., 2014).

For its potentially hostile and, at the same time, attractive

characteristics, a volcano’s environment can arouse a wide range of

emotions among visitors for the variety of landscapes the location

possesses (Bird, Gísladóttir, & Dominey‐Howes, 2011; Böhm, 2003;

Kaplan, 1987; Purcell, Peron, & Berto, 2001; Ruiz & Hernández,

2014; Zube, Pitt, & Anderson, 1975). Similarly, Mt. Etna is an

evocative environment that can provoke emotions among individuals

such as excitement, enchantment, or inner peace. Thus, Mt. Etna is an

environmental and natural site potentially capable of provoking an

experiential augmentation of the environment in which consumers

evaluate food products including wine.

3 | EXPERIMENTAL PROCEDURE

To assess the effects of experiential augmentation applied to the

environment of Mt. Etna on consumers’ WTP for wine produced

around the volcano, a nonhypothetical experimental auction was

designed and conducted. An experimental auction is used since it is a

well‐established method to assess the WTP for food products

(Akaichi, Nayga, & Nalley, 2017; Pappalardo & Lusk, 2016). In

addition, experimental auction simulates a real market where

consumers can make the decision to buy or not buy a good through

a real money transaction. For this reason, experimental auctions tend

to provide researchers with a more accurate estimate of product

values compared with hypothetical or stated preference methods

(Lusk & Shogren, 2007).

Previous studies have explored factors influencing experiential

consumption of wine by using different hypothetical methods. For

example, principal axis factoring (Marzo‐Navarro & Pedraja‐Iglesias,2012; Sparks, 2007) and principal component analysis methods

(Clemente‐Ricolfe, Escriba‐Pérez, Rodríguez‐Barrio, & Buitrago‐Vera,2012; Galloway et al., 2008) have been used to determine the

motivational factors of wine tourists. Comparatively, few studies

have used nonhypothetical methods to estimate the effects of

intangible factors in consumers’ WTP for wine. In this regard,

Bradley, McCluskey, and Patterson (2015) used an experimental

auction with real products and real money to test the effects of

information related to specific locations that recall the origin of

a wine.

In this study, we used the random nth price auction method

(Shogren, Margolis, Koo, & List, 2001) since this method is incentive‐compatible and widely used in many empirical valuation studies (e.g.,

Capra, Lanier, & Meer, 2010; Chern, Hong, & Liu, 2013; Huffman,

Shogren, Rousu, & Tegene, 2003). Participants do not know the

winning position until all bids have been submitted, thus removing

the competitive biases that might exist in other experimental auction

mechanisms, such as the second‐price auction (Shogren et al., 2001).

Moreover, past studies have shown that the evaluation of partici-

pants in the random nth price auction is impartial, accurate, and

tends to show the highest speed of convergence between the

participants’ WTP and the willingness to accept a good compared

with other auction methods (Lee, Han, Nayga, & Lim, 2011; List,

2003; Lusk et al., 2004; Parkhurst, Shogren, & Dickinson, 2004).

3.1 | Experimental treatments and implementationof the experimental auction

We conducted our experiment in Sicily, Italy, in May 2017. A total of

140 subjects in our study were recruited from a pool of students at

the University of Catania in Sicily. All subjects participating in the

research declared beforehand to be wine consumers. All the subjects

had the minimum age (18 years old) required by the Italian legislation

authorizing the consumption of alcohol (Law 30 March 2001 No. 125

“Framework Law on alcohol and related problems”).

The subjects were randomly assigned into two groups: “control”

and “treated” (Table 1). Each subject within the control group

participated in three treatments called Treatment Control 1 (TC1),

Treatment Control 2 (TC2), and Treatment Control 3 (TC3). Similarly,

each subject within the treated group participated in three

treatments called Treatment Treated 1 (TT1), Treatment Treated 2

(TT2), and Treatment Treated 3 (TT3). The three treatments within

each group were carried out with an interval of one week from one to

another during May 2017.1

The three treatments for the participants in the control group

(TC1, TC2, and TC3) were carried out inside a sensory lab, while

participants in the treated group received the first treatment (TT1) in

the sensory lab, the second treatment (TT2) in a winery on Mt. Etna,

and the third treatment (TT3) in the sensory lab previously used

during the first treatment. Ultimately, we implemented a mixed

1Unfortunately, probably for personal circumstances of participants, it was not possible at

the end of the experiment to have an equal number of subjects between the control and

treated groups. In fact, of the 140 subjects who were initially recruited and participated in

the first treatment (70 in the treatment TC1 and 70 in the treatment TT1), only 118 subjects

(66 in the control group and 52 in the treated group) participated in all three treatments. For

the purposes of the study, only data concerning the subjects who participated in all three

treatments were considered in the analysis.

644 | PAPPALARDO ET AL.

design divided into three phases, pretest, posttest, and follow‐up test,

where the repeated measure within the two groups is the WTP for

one 0.75 L bottle of wine. In our experiment, the experiential

environment was represented by a winery located around Mt. Etna.

By comparing pretest and posttest results, we can observe the effect

of the experiential augmentation on valuation of wine and by

comparing the results between the follow‐up and posttest, we can

observe whether the effects of the experiential augmentation persist

over time.

Subjects were informed in advance that they would participate in

three treatments and in each they would take part in an economic

experiment conducted using experimental auctions. Participants in

the control group were informed that they would perform all the

three treatments inside a sensory lab. Similarly, the participants in

the treated group were informed that they would perform the first

and third treatments in the sensory lab, while the second treatment

would be held inside a winery located around Mt. Etna.

More specifically, participants in the treated group carried out the

second treatment (TT2) inside a winery located around Mt. Etna that

produces Etna PDO wine. As soon as the participants arrived at the

winery and before the auction started, they were invited to take a

guided tour of the winery. The guided tour not only allowed participants

to observe the premises of the cellar where the wine is produced but

also represented an architectural, gustatory, and emotional journey for

the participants that revealed the majesty and elegance of the cellar and

the links between the cellar and Mt. Etna. After the 90‐min tour,

participants were then taken to the elegant premises of the barrel cellar

where the experimental auction took place.

Subjects were also informed that before the experimental

auctions both in the sensory lab and in the winery, they would taste

three types of Etna wine2 that were similar in terms of organoleptic

characteristics and year of production and were only distinguishable

nominally as “A,” “B,” and “C.” We used blinded tastings to ensure

impartiality and to avoid product‐related experiential effects, that is,

potential bias effects in the participants’ judgment due to product‐related attributes. We used three types of Etna wine to test the

consistency of the results across different types of wine in the region.

Participants were not informed that the wines they tasted originated

from Mt. Etna.

In all treatments, subjects participated in a random nth price

auction with five rounds of bidding in each treatment according to

the procedure described below:

Step 1: Upon arrival at the venue, after signing the consent form,

each bidder received an ID and was invited to sit in a pre‐establishedseat. We then proceeded to the blind tasting, which consisted of

tasting a 30ml sample for each of the three wines used in the

experiment.

Step 2: At the end of the tasting, the monitor clearly explained in

detail the random nth price auction mechanism to participants.

Step 3: To ensure participants understood how the auction

mechanism worked, we conducted a practice session with three

different 1 kg generic pasta packages. This auction was only a trial

session to familiarize the participants with the auction mechanism,

and at the end of this, nothing was bought. Since the real auction was

with three wines and five bidding rounds, we conducted the trial

auction with three different types of pasta and with five bidding

rounds.

Step 4: After completing the trial auctions, the real auction with

wine began, and each participant simultaneously submitted bids for

each of the three wines used in the experiment. The bids were for a

bottle of 0.75 L of wine “A”, “B,” and “C.” To avoid any issues of bias

or affiliation, participants did not receive any kind of feedback

between rounds, such as who was the winner or if the winning bid

represented the market price. Avoiding the provision of feedback

between the rounds in experimental auctions could help mitigate

bidders’ strategic behaviors (Corrigan, Drichoutis, Lusk, Nayga, &

Rousu, 2012).

Step 5: At the end of each round, bids were collected and ordered

from highest to lowest.

Step 6: At the end of the fifth bidding round, one of the three

wines was randomly drawn, and this wine was chosen as the

binding wine.

Step 7: The binding round was randomly drawn, from which the

price for the binding wine would be identified.

Step 8: The random nth price (market price), whose value was

between 2 and n, where n was the number of bidders in the auction’s

session (ranging in our experiment from 7 to 8), was randomly drawn.

Step 9: The random nth price was announced; the bidders who

have submitted a bid higher than the nth price won the auction.

Winning bidders pay the nth price to buy the randomly chosen bottle

in the randomly chosen round.

TABLE 1 Experimental treatments

Designs GroupsTreatmentdenomination

Experientialconsumption

Location oftreatment

Winetasting

Rounds in theauction

Pretest Control group TC1 No Sensory lab Blind 5

Treated group TT1 No Sensory lab Blind 5

Posttest Control group TC2 No Sensory lab Blind 5

Treated group TT2 Yes Winery Blind 5

Follow‐up test Control group TC3 No Sensory lab Blind 5

Treated group TT3 No Sensory lab Blind 5

2The wines used in our survey were: Etna Rosso Femina PDO, Etna Rosso Sensi PDO, and

Etna Rosso Vulcano PDO.

PAPPALARDO ET AL. | 645

Step 10: All bidders who had offered a price equal to or lower

than the market price did not buy anything.

Step 11: All participants filled out a follow‐up questionnaire

containing a series of questions related to emotions aroused by Mt.

Etna and demographics.

Each participant was given €20.00 at the end of all three

treatments for their participation.

4 | EXPERIMENTAL RESULTS

4.1 | Descriptive analysis

Summary statistics of the participants are shown in Table 2. The average

age of the subjects was 21.71 years in the control group and 22.27 years

in the treated group. Most of the subjects were female. The yearly

average household income ranged from €20,000 to €30,000. Most of the

participants (91%) indicated that they have visited Mt. Etna in the past.

As shown in the column of p value of t test, no significant differences

were found between the control and treated groups in regard to the

variables used in our analysis except for age.

Table 3 reports the average bids for the three wines across the

treatments. Consumers’ WTP for each wine in the treated group was

higher than the WTP in the control group both in the post‐ and follow‐up tests. On average, for wine A, subjects in TT2 are willing to pay

€2.98 for this bottle of wine in the experiential environment (cellar)

while those in TC2 are willing to pay €1.90 in the nonexperiential

environment (sensory lab). These results signify the effect Mt. Etna has

on consumers’ valuation for wine. Interestingly, WTP in TT3 was

higher than WTP in TT1 for all the three wines, reflecting the effect of

the previous experience of having visited the cellar.

We conducted a mean equality t test on WTP within the control and

treated group (i.e., within‐subjects analysis). The results of the t test,

illustrated in Table 4, show that there are no significant differences

between WTPs across the three treatments within the control group for

all the three examined wines. In contrast, the results of the t test within

the treated group show the existence of an effect due to the environment

in which consumers evaluated the wine. In fact, significant differences

were found in the treated group for wines A and C between the first

treatment performed in the lab (TT1) and the second treatment

performed in the winery (TT2). The lack of significance between TT1

and TT2 for wine Bmay be due to the intrinsic characteristics of this wine

that may not have been appreciated by the participants or to other

factors that are unclear to us. The difference in WTP between TT1 and

TT3 treatments for wine C was also significant. Importantly, there is no

significant difference in consumers’ WTP between TT2 and TT3

treatments, which suggests that the experiential effect still persists

among participants after they have visited the cellar. In other words, the

WTP recorded in TT2 persisted into the TT3 which was conducted in the

sensory lab.

We also conducted a mean equality t test on WTP between control

and treated groups (i.e., between‐subjects analysis; Table 5). The t test of

mean equality indicates that the estimated averageWTPs are statistically

different between control and treated groups only in between TC2 and

TT2 and between TC3 and TT3 across the three wines. Specifically,

results of the t‐tests show that there are no significant differences in

consumers’ WTPs between TC1 and TT1 treatments. Conversely, there

are significant differences in consumers’WTP between TC2 and TT2 and

between TC3 and TT3. These results again reflect the effect of the

experiential environment in which participants evaluated the wine

products. The statistically significant difference between TC3 and TT3

also supports the results of the within‐subject analysis that experiential

effects persist among participants after visiting the cellar.

4.2 | Effects of the experiential environment onconsumers’ WTP for wine

Given that the descriptive statistics and unconditional tests do not

completely reveal the effect of experience, we further analyze the

TABLE 2 Participants’ socioeconomic characteristics

Variables Categories

Controlgroup (66

units)

Treatedgroup (52

units) p value

Age (mean;

years)

21.71 22.27 0.0868*

Gender (%) Male 19.7 24.5 0.2592

Female 80.3 75.5

Income (%) <€20,000 37.9 32.7 0.2855

From €20,000

to €29,999

31.8 26.5

From €30,000

to €39,999

18.2 16.3

From €40,000

to 49,999

4.5 8.2

From €50,000

to 59,999

3.0 8.2

>€60,000 4.5 8.2

Visits to

Mt.

Etna (%)

Yes 90.9 91.8 0.7887

No 9.1 8.2

Note. p values in the last column represent the results of the mean

equality tests for each variable between the two groups.

*, **, and *** denote significance at 10%, 5%, and 1% levels, respectively.

TABLE 3 Mean bids of all five rounds across the treatments

Wine A Wine B Wine C

Treatments Mean SD Mean SD Mean SD

TC1 (pretest) 2.02 2.06 2.09 1.48 2.73 2.90

TT1 (pretest) 1.85 1.54 2.11 2.02 2.00 1.69

TC2 (posttest) 1.90 1.74 1.71 1.54 1.97 1.97

TT2 (posttest) 2.98 1.78 2.70 2.11 2.92 1.76

TC3 (follow‐up) 1.80 1.55 1.69 1.09 1.95 1.62

TT3 (follow‐up) 2.38 1.50 2.53 1.71 2.85 1.81

Note. Sample size: control group, 66 units; treated group, 52 units.

SD: standard deviation; TC1: Treatment Control 1; TC2: Treatment

Control 2; TC3: Treatment Control 3; TT1: Treatment Treated 1;

TT2: Treatment Treated 2; TT3: Treatment Treated 3.

646 | PAPPALARDO ET AL.

data at the individual level by estimating conditional regression

models. To estimate effects of the experiential environment of Mt.

Etna on consumers’ WTP for wine, we developed four regression

models described as follows.

In the first regression model, we captured the effects of the

treatments on consumers’WTP for wine by pooling the data from all six

treatments carried out by participants in the control and treated groups.

We then estimated a random effects regression model in which the time

dimension is the total number of rounds performed by each participant

in the three treatments, that is 15. The regression model is specified as

α β β β β

β β ε

= + + + + ×  

+ × + + +

 

 

BID A A TDum A TDum

A TDum X u

ij

i i ij

0 1 2 2 3 3 4 2

5 3 6

where BIDij is individual i's WTP for each wine (A, B, or C) in the round j

in both the control and treated groups; A2 is a binary variable = 1 if in

treatments TC2 and TT2 and 0 otherwise; similarly, A3 is a binary

variable = 1 if in treatments TC3 and TT3 and 0 otherwise; TDum is a

binary variable = 1 if participants belongs to the treated group and 0

otherwise; ×A TDum2 is an interaction term between A2 and TDum,

while ×A TDum3 is an interaction term between A3 and TDum. Xi

denotes a vector of control variables that include general socio-

demographic factors, ui is random effects which controls for unobser-

vable individual characteristics; and εij is the i.i.d. component.3

Results of this regression model show that participants’ WTP for

all three wines is affected by interaction terms “A2 × TDum” and “A3 ×

TDum” (Table 6). These results confirm the previous unconditional

analysis about the effects of the experiential environment in which

participants evaluated wine, for example, a winery recalling Mt. Etna.

In other words, having visited the winery has influenced consumers’

WTP for wine in the second treatment. Moreover, the effect of

having visited the winery influences consumers’WTP also in the third

treatment, and this could mean that the effects of the augmented

environment persist over time.

To further check the effects of experiential augmentation applied

to Mt. Etna, we estimated a second regression model. In this model,

we quantified the effect of Mt. Etna’s environment on consumers’

WTP for wine using only data from the treated group. We estimated

a random effects regression model in which the time dimension is the

total number of rounds performed by each participant in the first two

treatments of the treated group (TT1 and TT2), that is, 10. The

regression model is specified as

α β β ε= + + +  +BID TDum X uij i i ij0 1 2

where BIDij is the individual i’s WTP for each wine (A, B, or C) in the

round j in both the TT1 and TT2; TDum is a binary variable = 0 if in

TT1 and 1 if in TT2; Xi denotes a vector of control variables that

include general sociodemographic factors such as gender, age,

previous visits to Mt. Etna, and income level; ui is random effects

which controls for unobservable individual characteristics; and εij is

i.i.d. component.

TABLE 4 t Tests for equality of mean WTP within groups

Treatments

Wine A Wine B Wine C

Mean WTP differences p Value Mean WTP differences p Value Mean WTP differences p Value

TC1 and TC2 −0.11 1.000 −0.38 0.356 −0.76 0.157

TC1 and TC3 −0.21 1.000 −0.40 0.301 −0.78 0.134

TC2 and TC3 −0.10 1.000 −0.02 1.000 −0.03 1.000

TT1 and TT2 1.14 0.001*** 0.59 0.382 0.92 0.025**

TT1 and TT3 0.53 0.296 0.42 0.814 0.85 0.042**

TT2 and TT3 −0.61 0.168 −0.16 1.000 −0.06 1.000

Note. Sample size: control group, 66 units; treated group, 52 units.

TC1: Treatment Control 1; TC2: Treatment Control 2; TC3: Treatment Control 3; TT1: Treatment Treated 1; TT2: Treatment Treated 2; TT3: Treatment

Treated 3; WTP: willingness to pay.

*, **, and *** denote significance at 10%, 5%, and 1% levels, respectively.

TABLE 5 t Test for equality of mean WTP between groups

Treatments

Wine A Wine B Wine C

Mean WTP differences p value Mean WTP differences p value Mean WTP differences p value

TC1 vs. TT1 0.17 0.6232 −0.20 0.9514 0.73 0.1078

TC2 vs. TT2 −1.08 0.001*** −0.98 0.004*** −0.94 0.007***

TC3 vs. TT3 −0.57 0.046** −0.84 0.001*** −0.90 0.005***

Sample size: control group: 66 units, treated group: 52 units.

TC1: Treatment Control 1; TC2: Treatment Control 2; TC3: Treatment Control 3; TT1: Treatment Treated 1; TT2: Treatment Treated 2; TT3: Treatment

Treated 3; WTP: willingness to pay.

*, **, and *** denote significance at 10%, 5%, and 1% levels, respectively.

3We also developed the analysis using a random effects Tobit regression since some participants

submitted zero bids for the wines (around 3% of the total bids). The results from the random

Tobit models are reported in the appendix. The results are consistent with those in the text.

PAPPALARDO ET AL. | 647

Results from this regression model suggest that the environment

of Mt. Etna experienced by the treated group from the winery

positively influenced participants’ WTP for wine (Table 7), given that

the coefficients of the treatment variable “TDum” in all three wines

are positive and statistically significant.

We also developed a third regression model to quantify the effect

of Mt. Etna on consumers’ WTP for wine using only the Treatment 2

data from the treated and control groups. Specifically, we estimated a

random effects regression model referring only to the second

treatment and comparing results between the control and treated

groups (TC2 and TT2). The regression function is specified as

α β β ε= +  + + +BID CTDum X u2ij i i ij0 1 2

where BIDij is the individual i’s WTP for each wine (A, B, or C) in the

round j in both the TC2 and TT2; CTDum2 is a binary variable = 0 if in

TC2 and 1 if in TT2; Xi denotes a vector of control variables that include

general sociodemographic factors such as gender, age, previous visits to

Mt. Etna, and income level; ui is random effects which controls for

unobservable individual characteristics; and εij is the i.i.d. component.

Results from this regression model again suggest that the

environment of Mt. Etna positively affected participants’ WTP for

wine between control and treated groups (Table 8). The value of the

coefficient of the dummy variable “CTDum2” shows that participants’

WTPs in Treatment 2 in the treated group are higher by €0.98 for

wine A, €0.88 for wine B, and €0.80 for wine C, compared with the

WTPs in Treatment 2 in the control group.

Finally, we developed a fourth regression model to assess

whether the effect of Mt. Etna on consumers’ WTP for wine persists

after participants have visited the winery. Specifically, we estimated

a random effects regression model by using only data from the third

treatment and comparing results between control and treated groups

(TC3 and TT3). The regression function is specified as

α β β ε= +  + + +BID CTDum X u3ij i i ij0 1 2

where BIDij is the individual i's WTP for each wine (A, B, or C) in the

round j in both TC3 and TT3; CTDum3 is a binary variable = 0 if in

TC3 and 1 if in TT3. As in the previous regressions, Xi denotes a

vector of control variables that include general sociodemographic

TABLE 6 Random effects regression results of pooled data

Variables

Wine A Wine B Wine C

Coefficient p Value Coefficient p Value Coefficient p Value

Second auction (A2) −0.11 0.270 −0.38 0.000*** −0.76 0.000***

Third auction (A3) −0.21 0.038** −0.40 0.000*** −0.78 0.000***

Treated (TDum) −0.29 0.211 −0.07 0.769 −0.84 0.002***

Interaction (A2 × TDum) 1.25 0.000*** 0.96 0.000*** 1.68 0.000***

Interaction A3 × TDum) 0.74 0.000*** 0.82 0.000*** 1.64 0.000***

Gender −0.94 0.000*** −0.79 0.003*** −0.85 0.005***

Age 0.09 0.081* 0.05 0.391 0.08 0.206

Visits to Mt. Etna −0.42 0.258 −0.45 0.248 −0.41 0.359

Income −0.04 0.519 −0.01 0.876 −0.02 0.719

Cons. 1.22 0.332 2.121 0.108 2.10 0.160

Log likelihood −3148.24 −2998.34 −3452.46

Number of observations 1,770 1,770 1,770

Note. *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively.

TABLE 7 Random effects regression results between first and second treatments in the treated group

Variables

Wine A Wine B Wine C

Coefficient p Value Coefficient p Value Coefficient p Value

Treated (TDum) 1.14 0.000*** 0.59 0.000*** 0.92 0.000***

Gender −0.53 0.205 −0.60 0.275 −1.03 0.013**

Age 0.08 0.306 −0.03 0.717 0.06 0.370

Visits to Mt. Etna −1.45 0.043** −0.55 0.553 −0.90 0.203

Income −0.05 0.593 −0.06 0.632 −0.10 0.320

Cons. 2.03 0.271 4.01 0.094* 2.38 0.189

Log likelihood −786.08 −873.09 −825.38

Number of observations 520 520 520

Note. *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively.

648 | PAPPALARDO ET AL.

factors such as gender, age, previous visits to Mt. Etna, and income

level; ui is random effects which controls for unobservable individual

characteristics; and εij is the i.i.d. component.

Results from this regression model imply that the effect of the

environment of Mt. Etna persists in the third treatment, that is, after

participants have visited the winery (Table 9). The values of the

coefficients of the dummy variable “CTDum3” show that participants’

WTPs in the treated group in treatment 3 are higher by €0.45 for

wine A, €0.74 for wine B, and €0.76 for wine C compared with those

in the control group.

Overall, our results show that the environmental value of Mt.

Etna has provoked an experiential augmentation, which positively

affects consumer valuation of wine. This finding highlights the role

played by the environment in consumer buying behavior. In this

vein, our results confirm the findings of previous studies (Galloway

et al., 2008; Jang & Namkung, 2009; Platania, Platania, et al., 2016;

Sturiale & Scuderi, 2017); that is, the stimuli coming from the

consumption environment, for example, a winery or a wine shop,

can influence the emotional states of individuals and their

subsequent purchasing behavior. In fact, participants’ WTP was

higher when they evaluated wine within the experiential environ-

ment, that is, winery, compared with the WTP in the nonexper-

iential environment, that is, sensory lab. However, it seems

remarkable that in contrast to previous studies (Creusen et al.,

2018), our results reveal a positive relationship between partici-

pants’ WTP for wine and the environment in which participants

evaluated wine, even in the absence of product‐related experi-

ential attributes such as information on brand, packaging, and so

forth. This could mean that the environment in which consumers

evaluate wine directly affects experiential augmentation and,

ultimately, consumers’ WTP for wine.

We also checked if questions on the emotions related to Mt Etna

in the follow‐up questionnaire could affect consumers’ WTP for wine.

We found these to be not statistically significant. This result is

probably due to the quality of information we transmitted to the

participants. Nevertheless, identifying which emotions can be related

to the experiential augmentation of the environment is an important

research area that deserves further study.

5 | CONCLUSION

This study focused on assessing the effect of experiential augmenta-

tion of the environment in which consumers evaluate wine. Results

suggest that experiential augmentation related to the environment of

Mt. Etna was positively related with wine valuation. Specifically, in

the comparison between pretest evaluation (sensory lab) and

posttest evaluation (the ambiance of the winery) in the treated

TABLE 8 Random effects regression between control and treated groups in the second treatment

Variables

Wine A Wine B Wine C

Coefficient p Value Coefficient p Value Coefficient p Value

Control‐treated (CTDum2) 0.98 0.002*** 0.88 0.007*** 0.80 0.017**

Gender −1.25 0.001*** −1.27 0.001*** −1.35 0.001***

Age 0.04 0.603 −0.02 0.839 0.05 0.550

Visits to Mt. Etna −0.53 0.333 −0.14 0.797 0.33 0.567

Income −0.07 0.378 0.01 0.902 −0.03 0.707

Cons. 2.70 0.142 3.22 0.093* 1.76 0.373

Log likelihood −678.02 −709.96 −667.21

Number of observations 590 590 590

Note. *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively.

TABLE 9 Random effects regression between control and treated groups in the third treatment

Variables

Wine A Wine B Wine C

Coefficient p Value Coefficient p Value Coefficient p Value

Control‐treated (CTDum3) 0.45 0.090* 0.74 0.003*** 0.76 0.012**

Gender −1.30 0.000*** −0.77 0.008*** −0.85 0.019**

Age 0.05 0.411 0.08 0.179 0.12 0.105

Visits to Mt. Etna −0.48 0.298 −0.73 0.090* −0.87 0.103

Income −0.06 0.386 −0.04 0.512 −0.00 0.937

Cons. 2.26 0.149 1.26 0.388 0.72 0.686

Log likelihood −604.65 −457.24 −581.66

Number of observations 590 590 590

Note. *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively.

PAPPALARDO ET AL. | 649

group, we found a positive effect of the augmented environment.

Moreover, a comparison between control and treated groups showed

a positive effect of the experiential environment on participants’

WTP for wine. In addition, a comparison between the follow‐up tests

in both the control and treated groups, (third treatment in the

sensory lab) revealed that experiential augmentation of the environ-

ment in the posttest evaluation (second treatment in the winery)

persists over time.

Our findings highlighted an interesting aspect that is still

unexplored in the current scientific literature. Specifically, results

suggest that experiential augmentation of the environment

significantly affects consumers’ WTP for wine (without the

presence of product‐related experiential attributes such as brand

or product packaging), reflecting the relevant role played by

environmental attributes not pertaining directly to the wine

products. Moreover, experiential augmentation of the environ-

ment in which consumers evaluate wine seem to persist over time,

at least in this study.

Our results have important implications for the actors involved in

the wine sector. For example, for cellar managers, the adoption of

experiential marketing practices explicitly related to the environment

in which wine is consumed can lead to experiential augmentation that

could increase consumers’ valuation for the wine products. For wine

producers, the experiential augmentation of the environment can

lead to an increase in wine demand that could enhance farm income.

Hence, under the perspective of experiential marketing, our results

generally suggest that experiential augmentation applied to the

environment could be a tool that wine producers and marketers

could use to differentiate their products and increase consumers’

demand for their products.

Our findings could also have important implications for

marketers outside of the wine sector. By using the wine sector

as a setting for investigating a broader phenomenon that can occur

in other food sectors, we posit that experiential practices explicitly

related to the environment can elicit emotional states among

consumers that could significantly influence the demand for food

products.

However, given that this study is specific to the case of the Mt.

Etna region, future research should test the robustness of our

findings by assessing the effect of experiential augmentation of the

environment in other environmental contexts, for example, in other

wine regions characterized by high environmental values and also

with a broader population (i.e., nonstudents). Moreover, future

studies should also explore in more depth what emotions are related

to the environment upon which the experiential augmentation

occurs. Knowing which types of emotions can affect experiential

augmentation of the environment in which consumers evaluate foods

can help managers and marketers enhance the hedonic and symbolic

value of their food products.

ORCID

Gioacchino Pappalardo http://orcid.org/0000-0002-8312-0862

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652 | PAPPALARDO ET AL.

APPENDIX A

TABLE A1 Random effects Tobit regression results of pooled data

Variables

Wine A Wine B Wine C

Coefficient p Value Coefficient p Value Coefficient p Value

Second auction (A2) −0.09 0.398 −0.37 0.000*** −0.77 0.000***

Third auction (A3) −0.19 0.063* −0.39 0.000*** −0.77 0.000***

Treated (TDum) −0.38 0.113 −0.12 0.621 −0.97 0.001***

Interaction (A2 × TDum) 1.33 0.000*** 1.00 0.000*** 1.84 0.000***

Interaction (A3 × TDum) 0.84 0.000*** 0.88 0.000*** 1.78 0.000***

Gender −0.95 0.000*** −0.79 0.004*** −0.88 0.005***

Age 0.09 0.095* 0.04 0.503 0.07 0.260

Visits to Mt. Etna −0.44 0.247 −0.47 0.240 −0.41 0.376

Income −0.04 0.537 −0.00 0.963 −0.02 0.751

Cons. 1.25 0.335 2.31 0.090* 2.22 0.154

Log likelihood −3144.47 −3003.86 −3428.76

Number of observations 1,770 1,770 1,770

Note. *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively.

TABLE A2 Random effects Tobit regression results between first and second treatments in the treated group

Variables

Wine A Wine B Wine C

Coefficient p Value Coefficient p Value Coefficient p Value

Treated within 1.23 0.000*** 0.65 0.000*** 1.03 0.000***

Gender −0.58 0.195 −0.48 0.421 −1.07 0.016**

Age 0.07 0.343 −0.07 0.477 0.05 0.502

Visits to Mt. Etna −1.50 0.048** −0.69 0.490 −0.83 0.269

Income −0.06 0.558 −0.03 0.813 −0.10 0.341

Cons. 2.06 0.291 4.72 0.067* 2.52 0.193

Log likelihood −782.35961 −861.01708 −825.30

Number of observations 520 520 520

Note. *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively.

TABLE A3 Random effects Tobit regression results between control and treated groups in the second treatment

Variables

Wine A Wine B Wine C

Coefficient p Value Coefficient p Value Coefficient p Value

Control‐treated (CTDum2) 0.99 0.002*** 0.87 0.009*** 0.83 0.016**

Gender −1.27 0.001*** −1.22 0.002*** −1.38 0.001***

Age 0.04 0.619 −0.02 0.775 0.06 0.495

Visits to Mt. Etna −0.54 0.324 −0.17 0.762 0.30 0.609

Income −0.07 0.396 0.02 0.828 −0.03 0.747

Cons. 2.73 0.142 3.33 0.092* 1.56 0.441

Log likelihood −678.44 −711.22 −667.99

Number of observations 590 590 590

Note. *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively.

PAPPALARDO ET AL. | 653

TABLE A4 Random effects Tobit regression results between control and treated groups in the third treatment

Variables

Wine A Wine B Wine C

Coefficient p Value Coefficient p Value Coefficient p Value

Control‐treated (CTDum3) 0.46 0.084* 0.74 0.004*** 0.78 0.012**

Gender −1.31 0.000*** −0.79 0.008*** −0.86 0.018**

Age 0.05 0.402 0.08 0.187 0.12 0.110

Visits to Mt. Etna −0.49 0.291 −0.76 0.089* −0.88 0.101

Income −0.06 0.402 −0.04 0.570 −0.00 0.946

Cons. 2.22 0.161 1.26 0.402 0.74 0.684

Log likelihood −605.15 −467.91 −591.16

Number of observations 590 590 590

Note. *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively.

654 | PAPPALARDO ET AL.