Full Terms & Conditions of access and use can be found at
International Journal of Advertising
The Review of Marketing Communications
ISSN: 0265-0487 (Print) 1759-3948 (Online) Journal homepage: https://www.tandfonline.com/loi/rina20
Online shopper engagement in price negotiation:
the roles of culture, involvement and eWOM
Shalom Levy & Yaniv Gvili
To cite this article: Shalom Levy & Yaniv Gvili (2020) Online shopper engagement in price
negotiation: the roles of culture, involvement and eWOM, International Journal of Advertising, 39:2,
232-257, DOI: 10.1080/02650487.2019.1612621
To link to this article: https://doi.org/10.1080/02650487.2019.1612621
Published online: 10 May 2019.
Submit your article to this journal
Article views: 834
View related articles
View Crossmark data
Citing articles: 10 View citing articles
Online shopper engagement in price negotiation: the
roles of culture, involvement and eWOM
and Yaniv Gvili
Department of Economics and Business Administration, Ariel University, Ariel, Israel;
Business Administration, Ono Academic College (OAC), Kiryat Ono, Israel
The strategy of inviting online shoppers to negotiate product pri-
ces has been employed by numerous online sellers due to its
benefits for buyers and sellers. Social media facilitates sharing
information regarding such economic benefits among shoppers,
thereby generating eWOM, which boosts online social commerce.
Yet not all buyers choose to embrace sellers’offers to negotiate
product price. In the current paper, we employ consumer culture
theory and the elaboration likelihood model to theorize the
effects of culture and involvement on consumer engagement in
price negotiation. Two studies were designed to test the pro-
posed conceptual framework. Based on eBay transaction data
(N¼498), Study 1 supported the hypothesized positive main
effects of collectivism and involvement on shoppers’engagement
in price negotiation. Study 2 demonstrated, in a controlled labora-
tory setting (N¼468), the moderating effect of eWOM on these
relationships. When other buyers shared information regarding
price negotiation, the positive effect of collectivism on negoti-
ation was mitigated, and the effect of involvement was strength-
ened. These findings have significant theoretical, practical, and
Received 15 August 2018
Accepted 17 April 2019
In efforts to promote their business, online sellers often offer prospective buyers
opportunities to engage in price negotiation (Sharma and Krishnan 2001). In 2016,
when Facebook relaunched Marketplace, a new social e-commerce platform, it encour-
aged would-be buyers to contact sellers and engage in real-time price negotiation
using Messenger (Facebook 2018). Consumer Reports praised Marketplace and its sel-
lers for welcoming online shoppers to counter offers because its commercial data sug-
gest that haggling adds value to both buyers and sellers and boosts sales (Bufete
2016;2017). eBay sellers attract buyers by inviting them to engage in price negoti-
ation as a strategy to drive sales (Rampen 2016). As a result, in 58% of all eBay sales,
buyers are allowed to negotiate the product’s price (Hasker and Sickles 2010).
CONTACT Yaniv Gvili email@example.com School of Business Administration, Ono Academic College (OAC), 104
Zahal St., Kiryat Ono 55000, Israel.
ß2019 Advertising Association
INTERNATIONAL JOURNAL OF ADVERTISING
2020, VOL. 39, NO. 2, 232–257
Welcoming customer engagement in bargaining has also become common practice
for traditional retailers (Stout 2013), as customers have become more informed, inter-
connected with each other and increasingly aware of their negotiating clout (Prahalad
and Ramaswamy 2004).
Consumer engagement in price negotiation may be invigorated by the emerging
practice of consumer price posting, where people share their purchase price informa-
tion on social media. Using social apps and networked technology, customers learn
about the prices paid by others and share their own engagement experience (Larivi
et al. 2013). Based on such posts, consumers can engage more effectively in informed
price negotiation (Zhang and Jiang 2014).
Online customer engagement in price negotiation may be mutually beneficial to
both buyers and sellers because it enhances the shopping experience (Moon et al.
2013) and communicates to shoppers that sellers seek to engage in constructive dia-
logues (Colliander, Dahl
en, and Modig 2015), reinforcing buyer–seller relationships
(Chan, Cheng, and Hsu 2007; Rappaport 2010). In addition, inviting shoppers to nego-
tiate price increases customers’perceived value of the seller’s offering (Holmes
et al. 2017).
Despite its promising benefits, not all buyers and sellers find price negotiation
equally appealing (Denegri-Knott and Molesworth 2010; Standifird, Roelofs, and
Durham 2005). Some buyers prefer to eliminate haggling by using digital apps, due to
the anxiety they attribute to the negotiation process (Boudette 2017). Experienced
Airbnb hosts were also reported to disapprove haggling so extremely that they would
not book haggling guests even at full price (Porges 2016).
Building on consumer culture theory (CCT) and the elaboration likelihood model
(ELM), we propose that two key factors explain the differences in online shoppers’pro-
pensity to engage in price negotiation: collectivism-individualism, and involvement.
Furthermore, we argue that exposure to eWOM regarding others’negotiations with
sellers moderates these effects. Two studies were designed to test these theory-based
arguments. Study 1 uses data collected from transactions conducted on eBay to test
the main effects of culture and involvement on engagement in price negotiation.
Study 2 tests the hypothesized moderation effect of eWOM in a controlled labora-
This paper contributes to the body of literature on online consumer engagement
by investigating the antecedents of a particular consumer activity within this domain
–negotiating with online sellers. It also adds to the literature of social commerce mar-
keting by demonstrating the interaction effects of eWOM with cultural factors and
involvement on consumer acceptance of marketers’invitation to negotiate prices.
Price negotiation is a growing phenomenon in online and offline shopping processes
where seller-buyer interactions take place (Bauer, Falk, and Hammerschmidt 2006;
Fang 2006; Sun, Ni, and Wang 2016). Negotiation is defined as an interaction between
two or more parties to determine the terms of exchange (Mintu-Wimsatt and
Calantone 1996). During negotiations, both parties attempt to maximize their benefits
INTERNATIONAL JOURNAL OF ADVERTISING 233
from the transaction (Brett 2007; Gillison, Northington, and Beatty 2014; Zeng,
Dasgupta, and Weinberg 2012). The stronger one’s tendency to negotiate, the stronger
one’s competitiveness during this process (Graham, Mintu, and Rodgers 1994; Lee
2000). Potentially, consumers may benefit from negotiating product prices by counter-
offering while shopping. Yet, not all consumers choose to negotiate (De Kervenoael,
Hallsworth, and Elms 2014). Research suggests that cultural factors may be the under-
lying explanation for this divide (Ackerman and Tellis 2001).
The effect of culture on shopping behavior
Although the Internet and the worldwide popularity of its business and social applica-
tions have bridged geographic distances between buyers and sellers, it has not erased
the fact that online shopping is a global phenomenon that involves interactions
between buyers and sellers from different regions, nationalities, and cultures. From
marketing communications’perspective, the Web facilitates communications involving
diverse audiences, allowing sellers to attract buyers from various parts of the world.
Consequently, more online sellers endeavor to interact and transact with foreign
buyers. The Alibaba Group, for example, has adopted a global market strategy to pro-
mote the company’s business across the globe (Lashinsky 2017), and Amazon has
announced that its annual special Prime Day shopping event will be promoted in
more international markets than ever, including many European countries, India, and
China (Vanian 2018; Weinswig 2017). Consequently, an increasing proportion of online
shopper-seller interactions take place across cultures.
Culture is “a collective programming of the mind which distinguishes the members
of one group or category of people from those of another”(Hofstede 1991, p.5).
Culture has been traditionally conceptualized at the national level (Hofstede 2001) and
was later extended to the individual level (Yoo and Donthu 2005). National culture
refers to ‘the characteristics that create a [national] society’s profile, inclusive of norms,
values, and institutions’(Griffith, Yalcinkaya, and Rubera 2014, p.6). That is, values and
norms often vary across national societies. Nonetheless, similar cultural values were
found at the individual level across countries and nationalities (Minkov and Hofstede
2011; Taras, Kirkman, and Steel 2010; Yoo and Donthu 2005).
Cultural differences act as a major driver of consumer behaviour diversity (Hong,
Muderrisoglu, and Zinkhan 1987; Moon, Chadee, and Tikoo 2008; Pergelova and
Angulo-Ruiz 2017). Consumers from different cultures respond dissimilarly to identical
marketing stimuli (De Mooij and Hofstede 2010; Tsai and Men 2017), which requires
marketers to adjust their marketing communications to the cultural nuances of their
target audiences (Davis, Wang, and Lindridge 2008; Grau and Zotos 2016).
Research suggests that shopping behaviour is contingent on shoppers’culture
(Ackerman and Tellis 2001). The approach of consumer culture theory (CCT) provides a
theoretical explanation for this effect (Arnould and Thompson 2005; Holt 1997). CCT
conceptualizes culture as the foundation of a consumer’s experiences, interpreted
meanings, and actions (Geertz 2008). Culture frames consumers’scope of conceivable
action, emotions, and thought, making patterns of behaviour more likely than others
(Askegaard and Kjeldgaard 2002; Holt 1997; Thompson and Hirschman 1995). CCT
234 S. LEVY AND Y. GVILI
suggests that consumer culture furthermore affects shopping behaviours differently
across social spaces and contexts (e.g. offline shopping, digital markets, social network
site). We subscribe to this point of view and accordingly posit that consumer inten-
tions to engage in price negotiation –a particular aspect of shopping behaviour –are
influenced by the individual’s cultural orientation.
Culture and price negotiation
Past research has shown that people’s general tendency to negotiate is influenced by
their values, which are rooted in their national cultures (Buchan, Croson, and Johnson
2004; Chuah, Hoffmann, and Larner 2014). More specifically, cultural factors may affect
consumers’tendency to engage in price negotiation (Lee 2000; Nyer and
In his seminal work, Hofstede (2001) identified major cultural characteristics that dif-
ferentiate between societies. One of the main characteristics identified by Hofstede is
individualism vs. collectivism, which is defined as ‘people looking after themselves and
their immediate family only, versus people belonging to in-groups that look after
them in exchange for loyalty’(De Mooij and Hofstede 2010, p. 88). People from indi-
vidualistic societies value personal welfare, autonomy, independence and individual
decisions. As a result, when making purchase decisions, they prefer self-interest and
tend to rely on themselves, their own efforts (Griffith, Yalcinkaya, and Rubera 2014),
and their internally owned information rather than seek advice from their
On the other hand, people from collectivistic societies value social belonging, inter-
dependency, and group decisions. Highly collectivistic shoppers have a social obliga-
tion to share consumption experiences with in-group others (Pizam and Jeong 1996),
which enhances in-group social interaction and collaborative behaviours (Cai, Wilson,
and Drake 2000).
The literature on individualism-collectivism suggests that collectivists tend to
engage in competitive relationships with out-group individuals (Triandis 1990).
Accordingly, their relationships with out-group online sellers will be competitive in
nature. Research suggests that collectivist people tend to have less trust in out-group
others (Huff and Kelley 2005; Watkins and Liu 1996), and that their trust radius is usu-
ally narrower than individualists (Van Hoorn 2015). Marketing research shows that the
overall lower trust collectivists assign to out-group sellers further hurts their percep-
tion of product price fairness (Bolton, Keh, and Alba 2010). Therefore, collectivists are
more likely to engage in price negotiation than individualists as they are more sensi-
tive to price fairness.
In addition, experiencing shame evoked by "face" concerns may also increase the
collectivist shopper tendency to engage in price negotiation (Chuah, Hoffmann, and
Larner 2014). Collectivist consumers experience more shame when paying a higher (vs.
lower) price than a friend because of a perceived loss of social status resulting from ill
treatment by others (Bolton, Keh, and Alba 2010). Consequently, collectivist shoppers
will be more inclined than individualistic shoppers to engage in competitive price
negotiation (Lee 2000). Hence, we propose the following hypothesis:
INTERNATIONAL JOURNAL OF ADVERTISING 235
H1: Online shoppers from more collectivistic cultures will show a higher tendency to
engage online in price negotiation than shoppers from individualistic cultures.
The effect of involvement on price negotiation
Consumer involvement is a key factor that drives online interaction in general
(Voorveld, Neijens, and Smit 2009) and specifically interactions in online shopping con-
texts (Han and Kim 2017). Involvement refers to a person’s perceived relevance of an
object, topic, or purchase process to her own needs, values, and interests (Mittal and
Lee 1989; Zaichkowsky 1985). Involvement varies across domains, has multiple antece-
dents, and different consequences for consumer behaviour (Laurent and
We predict that shopper propensity to negotiate price is positively associated with
involvement in the transaction, for several reasons. First, involvement is positively
related to bidding behaviour on auction sites (Stafford and Stern 2002). Although
online bidding and price negotiation are distinct behaviours, they are similar in the
sense that they entail shoppers’online interaction with sellers aimed to lower purchas-
ing price. Second, consumers’primary motivation to negotiate a product price is to
gain better value (Sharma and Krishnan 2001). Under low involvement conditions, pur-
chase importance and its overall expected value are lower. Because negotiating with
sellers involves costs of time and effort, shoppers’willingness to engage in such a
behaviour is more likely to take place when its potential benefits are more significant:
that is, in high involvement contexts, when the transaction is more meaningful to the
buyer. Hence, the following hypothesis:
H2: Online shoppers’tendency to engage online in price negotiation will be positively
related to their involvement in the buying process.
Data collection and Sample: Transaction records of experienced eBay sellers were
chosen for the current study’s sample. eBay was chosen because of its innovative sys-
tem that provides historical records of seller-buyer interactions. Among e-commerce
websites, eBay presents the highest number of social commerce features (Curty and
Zhang 2013). The sampling frame was the overall annual transactions recorded by a
global eBay dealer who granted us access to account information for the purpose of
this study. A random sample of 498 transactions was sampled from approximately
15,000 transactions, such that it would equally contain transactions of buyers located
in the United States and Russia. Transactions were diverse and included products such
as perfumes, toys, and clothing. Product price range was GBP 3.20–74.70 (M¼12.93,
SD ¼10.91), with no difference between United States and Russia (M
¼11.11; t¼.48, p>.10).
Measurement procedure: Of the two sampled nationalities, Americans consistently
score higher than Russians on Hofstede’s individualism/collectivism index; for
236 S. LEVY AND Y. GVILI
Americans Hofstede’s index is 91, and for Russians the index is 39 (Hofstede Center
2018; Hofstede, Hofstede, and Minkov 2010). Following recent marketing research
(Amatulli et al. 2017), this procedure operationalized the national culture variable; cod-
ing was 1 for Americans and 2 for Russians. Additional measures were collected from
eBay dealer’s account and were coded as follows. Transaction involvement was meas-
ured directly by focusing on the behavioural aspects of involvement, which includes
information search and transaction-related activities during the buying process
(Lichtenstein, Netemeyer, and Burton 1995). In digital contexts, involvement is typically
measured by time spent, efforts undertaken (Hemetsberger 2003; Wu, Scott, and Yang
2013), and the extent of dependency between people’s actions (Ekman et al. 2012).
Accordingly, in this study, involvement was measured by the number of shoppers’
reminder notes to sellers during the transaction process. The validity of this construct
measurement was supported by correlating it with another transaction-related activity
–buyer feedback (r¼.20, p<.01). A dichotomized measure was coded 1 for high
involvement –for those who sent sellers reminder notes, and 0 otherwise. The
dependent variable, tendency to engage online in price negotiation, was measured by
shoppers’actual negotiation behaviour, where 1 indicates shoppers’price counter-
offering and 0 otherwise.
We controlled for variables suggested by research as potentially confounding with
consumers’price negotiation, such as product price (Bolton, Warlop, and Alba 2003;
Carlson, Huppertz, and Neidermeyer 2008; Sharma and Krishnan 2001) and customer’s
experience with the shopping site (Barrutia and Espinosa 2014; Dai, Forsythe, and
Kwon 2014; Hern
enez, and Mart
ın2010). Price was measured by the transac-
tion price in GBP. Shopper’s overall experience was measured by assessing two
aspects: shoppers’activity level and their experience with eBay. Shopper’s activity level
was denoted by eBay stars, which indicate buying frequency. This was coded as highly
active for shoppers with more than 300 stars, and less active otherwise. Shopper’s
experience was coded as low for shoppers that joined eBay in the last three years,
and high otherwise. Categorizing these variables was based on interviews conducted
with three eBay expert dealers.
First, Spearman’s nonparametric correlations were calculated to inspect the associa-
tions between variables of interest (Table 1). Next, logistic regression was employed to
test the hypothesized relationships, while controlling for price and shopper’s
Table 1. Non-parametric correlations.
1. Engagement in price negotiation .604 .123 .119 –.127 .286
2. Transaction involvement 1.00 .093.080 –.110.250
3. Shopper’s activity –1.00 .295 –.046 –.153
4. Experience ––1.00 .117 .026
5. Price –––1.00 –.033
6. Country ––– –1.00
Notes:N¼498; <.05, <.01.
INTERNATIONAL JOURNAL OF ADVERTISING 237
experience. The results (Table 2) indicate that an adequate share of the variance is
explained by the suggested model’s independent variables (Cox and Snell’sR
¼.48). As expected, country (as indicator for national culture) has a
positive significant effect (p<.01) on tendency to engage in online price negotiation,
which means that Russians are more inclined to negotiate than Americans. Transaction
involvement also has a significant effect (p<.01) on tendency to engage in online
price negotiation. These findings provide preliminary support for Hypotheses H1
The purpose of this study is to examine the effect of shoppers’culture and product
involvement on engagement in price negotiation on online shopping sites. Our find-
ings suggest that Russian shoppers (i.e. from collectivist societies) were more inclined
to engage in price negotiation than American shoppers (i.e. from individualistic soci-
eties). This result is in line with consumer culture theory, which suggests that culture
is a major predictor of consumer shopping behaviour (Arnould and Thompson 2005),
and specifically that American consumers refrain from price negotiation (Herrmann
2003). Moreover, in accordance with the literature on consumer negotiation behavior,
the results support earlier findings that collectivism encourages shoppers’online nego-
tiation behavior (Bolton, Warlop, and Alba 2003; Chuah, Hoffmann, and Larner 2014;
Lee 2000) and extends this literature to online shopping contexts.
Interestingly, although American shoppers were more experienced (Table 1), they
were less inclined to negotiate prices than Russian shoppers. One explanation for this
may be rooted in Triandis’s(2001) proposition that highly collectivist people are less
inclined to resolve conflicts personally, and prefer to settle disputes through an
authoritative mediator or intervention.
Following previous research (Kim, Sung, and Drumwright 2018; Pergelova and
Angulo-Ruiz 2017), Study 1 employs country as an indicator for the different cultural
values of individualism/collectivism. Yet, country is not a direct measurement of cul-
tural values. Thus, although the results imply a relationship of national-cultural values
and shopper tendency to engage in online price negotiation, this design may limit our
conclusions only to what the shopper’s country actually reflects. Study 2 provides a
remedy by directly measuring cultural values (individualism/collectivism) in a
An alternative explanation for the variation in price negotiation between Russia and
the U.S. is rooted in shopper income across countries. When shopper income is lower,
Table 2. Logistic regression of negotiation disposition.
Variable B S.E. Wald df Sig Exp(B)
Constant –2.793 0.363 58.197 1 0.000 0.061
Transaction involvement 2.873 0.266 116.253 1 0.000 17.688
Shopper’s activity 0.601 0.278 4.665 1 0.031 1.825
Experience 0.462 0.308 2.241 1 0.134 1.587
Price –0.024 0.013 3.650 1 0.056 0.976
Country 1.144 0.272 17.655 1 0.000 3.138
(5) ¼203.68, p<0.01; R
(Cox and Snell) ¼0.336; R
238 S. LEVY AND Y. GVILI
they may be more inclined to spend time on negotiating price. As shopper income
grows such behaviour makes less economic sense. To examine this alternative explan-
ation, we tested the effect of culture on shopper propensity to price negotiate while
controlling for shopper income in Study 2.
Additionally, involvement was found to be positively associated with price negoti-
ation. The literature on consumer online behaviour suggests that involvement enhan-
ces consumer interaction with websites and brands (Voorveld, Neijens, and Smit 2009).
The current research shows that involvement further translates into interaction with
the sellers themselves in effort to reduce price.
On social commerce platforms, shoppers are potentially exposed to other users’
generated content (UGC), and electronic word of mouth (eWOM), including reviews
and recommendations regarding products, brands and sellers (Edelman, Jaffe, and
Kominers 2016). Next, in Study 2, we test the effect of shared eWOM on negotiation
behaviors in online shopping contexts.
eWOM as a signal of economic benefits to customers
New technologies and social media platforms facilitate eWOM –content shared by
customers about a product or a company with a multitude of people and institutions
via the Internet (Hennig-Thurau et al. 2004). Essentially, eWOM involves sharing and
exchanging marketing information regarding brand- and seller-related experiences,
with other consumers in online environments (Chu and Kim 2018). In the past few
years, eWOM communications have accelerated in scope, such that people’s private
information regarding brands, products, and sellers is extensively shared on a global
scale (Araujo, Neijens, and Vliegenthart 2017; De Veirman, Cauberghe, and
Shared information consists of various content types and formats, including textual
reviews (De Pelsmacker, Dens, and Kolomiiets 2018), pictures (De Veirman, Cauberghe,
and Hudders 2017; Evans et al. 2017), videos (Hayes, Shan, and King 2018; Schivinski,
Christodoulides, and Dabrowski 2016), and promotional materials. Promotional eWOM
involves shared information about potential economic benefits to customers (e.g. price
discounts, sellers’willingness to negotiate price) (Vermeir and Van Kenhove 2005) that
result from lowering the costs incurred by the buyer (Chandon, Wansink, and
eWOM’s economic benefits as a driver of customer engagement
eWOM activity is a major driver of customer engagement behaviours (Van Doorn et al.
2010). Engagement accounts for consumers’interactive brand-related experiences
(Brodie et al. 2013) and has been conceptualized as a psychological-experiential pro-
cess (Calder and Malthouse 2008; Hollebeek 2011), and as a set of activities consumers
perform as they interact with brands and marketers (Rossmann et al. 2016; Shin
et al. 2016).
INTERNATIONAL JOURNAL OF ADVERTISING 239
Following Gavilanes et al. (2018), we focus on the interactive-behavioural dimension
of consumer engagement rather than on the psychological state, as this approach bet-
ter suited to capture the interactive nature of digital social media (Calder, Malthouse,
and Schaedel 2009). Consumer engagement in digital settings is highly depended on
the platform (Gvili and Levy 2018; Voorveld et al. 2018). Therefore, due to the nature
of social commerce and e-commerce platforms (e.g. Facebook Marketplace, eBay),
engagement in this setting is also expressed as customer interaction with sellers in
product price negotiation aimed to lower product price.
Consumers’interest in lowering the price paid in a particular transaction constitutes
a basic financial motivation of engagement behaviour (Pentina, Guilloux, and Micu
2018). Prior to making purchases, consumers often search on various electronic and
social commerce sites (e.g. eBay, GasBuddy) for information that can help them lower
product price (Edelman, Jaffe, and Kominers 2016). Many marketers provide such infor-
mation by posting promotional messages on their brands’social media pages (Schultz
and Peltier 2013). Once obtained, consumers may share this information with social
media tools (Fu, Wu, and Cho 2017).
The effect of eWOM on the relationship between collectivism and engagement
in price negotiation
The results of Study 1 show that collectivism is positively related to engagement in
price negotiation. We suggest that eWOM on others’experiences of price negotiations
with a seller moderates this relationship. Collectivist shoppers tend to receive and
share information from related others and use this information as a basis for decision
making (Pizam and Jeong 1996). Hence, the effect of the additional information they
may receive from outgroup others on social commerce site may be marginal. In con-
trast, individualistic consumers prefer to rely on knowledge they collect themselves
than seek information from related others (Griffith, Yalcinkaya, and Rubera 2014).
Therefore, the impact of eWOM is expected to be more profound for individualistic
consumers. Hence, the following hypothesis:
H3: eWOM concerning others’experiences of price negotiations with sellers will moderate
the relationship between collectivism and engagement in price negotiation. When such
eWOM is shared, this relationship will be stronger for individualist shoppers.
The effect of eWOM on the relationship between involvement and engagement
in price negotiation
As predicted above and supported by the results of Study 1, involvement is positively
related to engagement in price negotiation. We suggest that this relationship is, how-
ever, more complex and contingent on eWOM concerning others’negotiations. Such
information shared with prospective shoppers signals that price negotiation is possible
and potentially welcomed by the seller. In practice, according to the Elaboration
Likelihood Model (ELM) (Petty, Cacioppo, and Schumann 1983) individuals are more
likely to be persuaded by this message to engage in price negotiation when they are
highly involved. Under high involvement conditions, consumers tend to take the
240 S. LEVY AND Y. GVILI
central route to persuasion, where they process stimuli (i.e. eWOM) more carefully,
and consider the true merits of the available information. Consequently, the potential
benefits of negotiating price become more salient and influential, and negotiation is
likely to be selected more often. When information concerning others’price-negoti-
ation experience is not shared, it will not be available to influence prospective shop-
pers’decision to participate in price negotiation. As a result, the relationship between
involvement and engagement in price negotiation will be stronger when relevant
eWOM is shared. Hence, the following hypothesis:
H4: eWOM concerning others’experience of negotiating price with sellers will moderate
the relationship between involvement and engagement in price negotiation. When such
eWOM takes place, the relationship between involvement and engagement in price
negotiation will be stronger compared to situations with no such eWOM available.
The overall conceptual framework is illustrated in Figure 1.
Experimental design and procedure
An experimental design was used to test the research hypotheses, Mock eBay product
pages were designed for three products (a laptop, heartrate tracker and a headset).
These pages included product information, including a picture, price, and a button
inviting them to negotiate product price (see Appendix).
Participants were randomly assigned to one of two experimental conditions. In
both conditions, participants were exposed to reviews including general information
about the seller (e.g., seller’s rating, other products available). In condition 1 (treat-
ment), participants were provided with additional information shared online by other
eBay users: ‘Other buyers have negotiated the price with this seller. Some have
Figure 1. Conceptual Framework.
INTERNATIONAL JOURNAL OF ADVERTISING 241
managed to reduce the selling price’. In condition 2 (control), this shared information
was unrelated to price negotiation: ‘Other buyers have selected expedited shipping
method when ordering from this seller. Some have received the order in 24 hours’.
Executional cues were identical across the two experimental conditions.
First, participants completed general questions about their experience and history
with shopping sites and eBay in particular. To avoid demand effect bias, participants
were told that this study was part of a research concerning online shopping site
design. Next, they were presented with one mock eBay page and asked to imagine
that they were interested in buying this product on eBay from this seller, and that the
seller, price, delivery terms and warranty suit their needs and desires. Finally, they
were asked to follow instructions, and then complete a questionnaire.
The survey instrument comprised multiple items designed to measure the study varia-
bles (see Table 3). For culture, we used a six-item scale of collectivism taken from Yoo,
Donthu and Lenartowicz (2011). Involvement with the transaction scale was based on
Dholakia (2001). Shoppers’overall experience with online shopping was measured
using two scales: online shopping activity, which was measured with three items taken
from Khalifa and Liu (2007), and experience, which was measured with three items
based on Simonin and Ruth (1998). Finally, tendency to engage online in price negoti-
ation was measured with three items based on Magee, Galinsky, and Gruenfeld (2007)
and Reif and Brodbeck (2017). Participants’agreement with the items was measured
on a seven-point Likert scale from 1 (strongly disagree)to7(strongly agree).
Demographic variables were also gathered.
Table 3. CFA –Items’factor loading and variables’reliability and validity measures.
Variables and Items
Coef. AVE CR
Engagement in price negotiation .75 .90 .87
1. I am likely to negotiate the price of the product .76
2. I will probably negotiate the price of the product .95
3. It is possible that I will negotiate the price of the product .88
Transaction involvement .72 .88 .89
1. I have a high level of interest in the purchase process of this product .67
2. I would put a lot of effort into the purchase of this product .97
3. It is important to me to complete the transaction appropriately .87
Shopper’s activity .81 .93 .93
1. I shop extensively online .93
2. I shop online for a long time .84
3. I shop online frequently .92
Shopper’s experience .76 .91 .90
1. I am experienced with eBay’s alternative payment options .88
2. I am experienced with eBay buyer’s alternate options to set prices .82
3. I am experienced with eBay’s alternative shipping options .92
Collectivism .52 .87 .86
1. Individuals should sacrifice self-interest for the group .63
2. Individuals should stick with the group even through difficulties .62
3. Group welfare is more important than individual rewards .89
4. Group success is more important than individual success .79
5. Individuals should only pursue their goals after considering the welfare of the group .61
6. Group loyalty should be encouraged even if individual goals suffer .75
Standardized Coefficients, p<.01; AVE ¼Average Variance Extracted; CR ¼Composite Reliability.
242 S. LEVY AND Y. GVILI
A nationally representative sample of adults was recruited from the leading Israeli
Internet online panel in exchange for payment. Israel is used as the social-cultural con-
text for Study 2 because it allows studying consumers with various degrees of orienta-
tions toward individualism/collectivism (Ruvio 2008). This nature of Israel as a
culturally heterogeneous country (Abbas and Mesch 2015) is also supported by the
fact that its individualism/collectivism index is 54 (Hofstede Center 2018)–right in the
middle of the Individualism-Collectivism spectrum (Ruvio and Shoham 2007).
Therefore, a representative sample of individuals drawn from Israel will spread above
and below the mid-point of the scale. This sampling frame provides access to partici-
pants with various degrees of individualism/collectivism.
A total of 468 individuals participated in the online experiment; 235 were randomly
assigned to the treatment condition, and 233 were assigned to the control condition.
The age of participants ranged from 18 to 70 years (M¼33.4, SD ¼11.63). Forty-four
per cent of the respondents were males. Most of the participants had average income
or above (76%); and post-secondary education (74%).
A manipulation check procedure was applied to validate the experimental conditions
using items measured on a seven-point Likert scale from 1 (strongly disagree) to 7
(strongly agree). Participants were asked about the information shared by others dur-
ing the transaction (‘Other shoppers shared with me their experience of product price
negotiations with the seller’). As expected, participants in the treatment group
reported receiving more information from others about price negotiation, compared
to participants in the control group (M
¼1.93; t¼7.42, p<.01). No difference was found for the extent to which the
product page was perceived as realistic (‘The above transaction on eBay site seems
realistic to me’;M
t¼1.29, p>.05), the offer’s relevance to participants (‘This eBay transaction could be
relevant to me’;M
t¼0.84, p>.05) or its importance to the participants (‘This eBay transaction would be
important to me, provided that I needed the product’;M
¼1.80; t¼0.41, p>.05). Finally, no difference
was found between the treatment and control groups in terms of time (in seconds)
dedicated to inspect the stimuli (M
¼26.58; t¼–.61, p>.1). These results validate the research conditions.
Measures validity and reliability
First, items were subjected to an exploratory factor analysis (EFA) with Varimax rota-
tion. The analysis produced five factors, explaining 75.64% of the cumulative variance.
Item loadings were all above .60. Next, to confirm construct validity, a confirmatory
factor analysis (CFA) was performed. The results show acceptable fit for all measure-
value (124) ¼242.56, p<.05 (v
/df <2); Comparative Fit Index (CFI) ¼0.98;
INTERNATIONAL JOURNAL OF ADVERTISING 243
Normed Fit Index (NFI) ¼0.96; and Root Mean Square Error of Approximation
(RMSEA) ¼.05). All five construct standardized regression estimates were above .50,
reflecting acceptable fit of the measures. Average Variance Extracted (AVE) and com-
posite reliability (CR) were also calculated and indicated convergent validity. AVE val-
ues were .52, .72, .75, .81 and .76 for collectivism, transaction involvement, tendency
to negotiate, online shopping activity, and experience, respectively. CR values were
.87, .88, .90, .93 and .91, respectively. Internal consistency of the measurements was
further examined using Cronbach’s alpha. The results show acceptable reliability of the
measurements: .86 for collectivism, .87 for transaction involvement, .89 for tendency
to negotiate, .93 for online shopping activity and .90 for experience. Thus, the above
measures exhibit acceptable levels of validity and reliability. AVE values were greater
than the square of the correlation estimate between any pair of these constructs in all
cases. This further verifies the discriminant validity of the constructs. The correlation
pattern between variables and the Maximum Shared squared Variance (MSV) are pro-
vided in Table 4.
ANOVA results show that participant tendency to engage in online price negotiation is
higher under treatment (eWOM sharing) than control (no eWOM sharing) conditions
(F(1, 466) ¼14.24, p<.01). No significant differences were found for any other variable.
See Table 5 for the results and descriptive statistics.
A path analysis was conducted to test the study’s hypotheses. A three-step proced-
ure (Cortina, Chen, and Dunlap 2001) was employed to standardize the relevant inde-
pendent variables and create interaction variables for the moderation analysis. The
path analysis results show that the overall fit statistics (goodness of fit measures)
exhibit an acceptable level of fit (v
value (239) ¼481.88, v
/df ¼2.02, p<.05;
CFI ¼0.96; NFI ¼.92; RMSEA ¼.05), indicating that the path model is valid. The path
model, regression standardized coefficients, and their significance are depicted in
Table 6 shows the variables’direct relationships and the statistical measures. Figure
2depicts a positive direct relationship between collectivism and tendency to engage
online in price negotiation (b¼.29, p<.01); however, the relationship between trans-
action involvement and tendency to negotiate is marginal (b¼.14, p<.1). A positive
relationship was also found between eWOM sharing and tendency to engage online
Table 4. Correlations and the maximum shared squared variance (MSV)
Variable 1 2 3 456789
1. Engagement in price negotiation .75 .274 .074 .070 .105.147 .182 .033 .009
2. Transaction involvement .08 .72 .245 .193 .219 .106.029 .062 .020
3. Shopper’s activity .01 .06 .81 .492 .035 .056 .012 .125 .052
4. Shopper’s experience .00 .04 .24 .76 .066 .093.069 .059 .176
5. Price .01 .05 .00 .00 –.121 .005 .005 .023
6. Collectivism .02 .01 .00 .01 .01 .52 .016 .038 .016
7. eWOM Sharing .03 .00 .00 .00 .00 .00 –.043 .011
8. Income .00 .00 .02 .00 .00 .00 .00 –.069
9. Religiousness .00 .00 .00 .03 .00 .00 .00 .01 –
Notes:N¼468; <.05, <.01;
Correlations are in the upper right side while the MSV are in the lower left side;
AVE are in bold diagonal.
244 S. LEVY AND Y. GVILI
in price negotiation (b¼.17, p<.01), which suggests that eWOM sharing increases
people’s tendency to engage online in price negotiation. The results further show no
significant effect of the control variables on tendency to engage in price negotiation
(price (b¼.02, p>.05), shopping activity (b¼.03, p>.05), experience (b¼.02,
p>.05), income (b¼.00, p>.05), and religiousness (b¼.02, p>.05)). Variance Inflation
Table 5. Descriptive Statistics and ANOVA Results.
Variable Condition N Mean SD Min Max F
Engagement in price negotiation Control 233 3.77 1.76 1.00 7.00
Treatment 235 4.39 1.79 1.00 7.00
Total 468 4.08 1.80 1.00 7.00 14.24
Transaction involvement Control 233 5.22 1.53 1.00 7.00
Treatment 235 5.05 1.74 1.00 7.00
Total 468 5.14 1.64 1.00 7.00 1.26
Shopper’s activity Control 233 3.96 1.85 1.00 7.00
Treatment 235 4.00 1.90 1.00 7.00
Total 468 3.98 1.87 1.00 7.00 0.07
Shopper’s experience Control 233 3.34 1.69 1.00 7.00
Treatment 235 3.64 1.91 1.00 7.00
Total 468 3.49 1.81 1.00 7.00 3.14
Price Control 233 1433.48 1162.85 100 2600
Treatment 235 1450.64 1165.32 100 2600
Total 468 1442.09 1162.88 100 2600 0.03
Collectivism Control 233 4.19 1.27 1.00 7.00
Treatment 235 4.16 1.33 1.00 7.00
Total 468 4.18 1.30 1.00 7.00 0.05
Income Control 233 3.75 1.79 1.00 7.00
Treatment 235 3.88 1.70 1.00 7.00
Total 468 3.81 1.75 1.00 7.00 0.69
Religiosity Control 233 3.44 2.00 1.00 6.00
Treatment 235 3.40 1.90 1.00 6.00
Total 468 3.42 1.95 1.00 6.00 0.04
Figure 2. The moderating Role of eWOM Sharing: A Path Model.
Path parameters are standardized parameter estimates. p<.1; p<.05; p<.01
INTERNATIONAL JOURNAL OF ADVERTISING 245
Factors (VIF) for all variables, including the interactions, were all below the threshold
levels (VIF <3, Hair et al. 2010), indicating there are no issues of multicollinearity.
Additionally, the regression results show a moderation effect of eWOM sharing. The
eWOM sharing and collectivism interaction variable has a negative relationship with
tendency to engage online in price negotiation (b¼.22, p<.01). This indicates that
eWOM sharing dampens the positive relationship between collectivism and tendency
to engage online in price negotiation. That is to say, a less collectivist (or more indi-
vidualist) culture has a stronger positive effect on tendency to engage online in price
negotiation when eWOM concerned others’negotiations is shared (see Figure 3).
In contrast, the interaction variable of eWOM sharing and transaction involvement
has a positive relationship with tendency to engage online in price negotiation
(b¼.17, p<.05). This indicates that eWOM sharing strengthens the positive effect of
transaction involvement on tendency to engage in price negotiation. That is, when
eWOM is shared, the positive effect of involvement on tendency to negotiate price is
stronger when transaction involvement is high (Figure 4).
In addition, we performed ANCOVA on the effect of eWOM sharing on price negoti-
ation. The following variables were included as covariates in the model: transaction
Table 6. Model’s path relationships.
Standardized Effect Regression Weights
Direct Estimate C.R. p
Collectivism !Engagement in price negotiation .286 .403 3.696 <.001
Involvement !Engagement in price negotiation .136 .177 1.792 <.1
eWOM !Engagement in price negotiation .171 .502 3.777 <.001
eWOM Collectivism !Engagement in price negotiation .216 .439 2.991 <.01
eWOM Involvement !Engagement in price negotiation .169 .331 2.310 <.05
Shopper’s activity !Engagement in price negotiation .030 .023 .538 >.1
Shopper’s experience !Engagement in price negotiation .022 .018 .400 >.1
Price !Engagement in price negotiation .021 .000 .480 >.1
Income !Engagement in price negotiation .005 .004 .102 >.1
Religiousness !Engagement in price negotiation .019 .015 .430 >.1
Figure 3. The moderation effect of eWOM sharing on the relationship between shopper collectiv-
ism and engagement in price negotiation.
246 S. LEVY AND Y. GVILI
involvement, shopper’s activity, shopper’s experience, price, collectivism, income, and
religiosity. In line with the path analysis, the results revealed that collectivism and
involvement are significantly related to a tendency to engage in online price negoti-
(1, 457) ¼8.03, p<.01); F
(1, 457) ¼28.14, p<.01)). Further,
both interactions were significant (F
(1, 457) ¼5.24, p<.05;
(1,457) ¼8.60, p<.01).
We further tested for these moderation effects using Hayes (2013) PROCESS macro
for Model 1 with 5000 bootstrapped samples, with measured covariate values condi-
tioned at one SD above or below the mean. Results showed evidence for a significant
moderating effect of eWOM sharing on the relationship between collectivism and ten-
dency to engage online in price negotiation (B¼.28; t¼2.29; p<.05).
We then tested the conditional effects (simple slopes) of collectivism at the two lev-
els of eWOM sharing. Under the condition of no eWOM sharing (control), the relation-
ship between collectivism and tendency to engage in price negotiation was significant
(B¼.31; t¼3.54; p<.01); in contrast, under the condition of eWOM sharing (treat-
ment) this relationship was not significant (B¼.03; t¼.40; p>.10). The relationship of
product involvement and price negotiation was also moderated by eWOM sharing
(B¼.28; t¼2.93; p<.01). Under the no eWOM sharing condition (control), the relation-
ship between involvement and tendency to engage in price negotiation was margin-
ally significant (B¼.13; t¼1.74; p<.10); under the condition of eWOM sharing
(treatment), this relationship was significant (B¼.41; t¼6.25; p<.01). According to
these results, hypotheses H3 and H4 are supported.
This study demonstrates the significant impact of eWOM on online shoppers’inclin-
ation to accept a marketer’s invitation to negotiate product price. Study 2 replicates
the main effects found by Study 1, and further shows that these relationships are
moderated by shared eWOM. Information about other buyers’experiences with the
Figure 4. The moderation effect of eWOM sharing on the relationship between transaction invovle-
ment and engagement in price negotiation.
INTERNATIONAL JOURNAL OF ADVERTISING 247
seller may mitigate cultural effects on shopping behavior. Specifically, sellers’invita-
tions to negotiate product price tend to be accepted more often by individualists
under conditions of eWOM sharing. This suggests that eWOM represents the collective
wisdom that individualistic shoppers lack. The additive contribution of eWOM to col-
lectivistic shoppers is probably not significant in terms of its effect on their negoti-
ation behavior. In addition, since individualistic shoppers lack the collective wisdom
mentioned above, they may be more aroused by eWOM shared by other shoppers.
This, in turn, leads to stronger and more positive responses to the message (Hartmann
et al. 2014; Ladhari 2007).
In line with ELM, Study 2 shows that the impact of eWOM in supporting shopping
decisions is more profound under high involvement contexts. Under such conditions,
consumers are more inclined to process arguments (e.g., eWOM) that signal economic
merits and consequently negotiate product price with sellers.
The aim of this research was twofold: First, to examine the effect of culture and
involvement on shoppers’tendency to engage in product price negotiation, and
second, to examine the moderating effect of eWOM on these relationships. The first
study demonstrated that national culture affects shoppers’price negotiation behav-
iour. Collectivistic shoppers were found to negotiate product prices more than indi-
vidualistic shoppers. This study also confirmed the pivotal role of involvement in
shopper’s engagement in product price negotiation. Shoppers who were highly
involved in the transaction engaged more with price negotiation than those who
were less involved.
In Study 2, an online experiment confirmed the results of Study 1 and showed the
moderating effect of eWOM sharing on shopper’s engagement in product price nego-
tiation. Apparently, sharing eWOM on social commerce platforms bridges the informa-
tional gap between individualist and collectivist shoppers, as the former tend to share
less in-group information (Griffith, Yalcinkaya, and Rubera 2014).
This study also shows that, under conditions of eWOM sharing, shoppers who are
highly involved in the transaction negotiate price more than those who are less
involved. As proposed by ELM (Petty, Cacioppo, and Schumann 1983), higher involve-
ment leads to higher motivation to process eWOM messages more carefully (treating
them as central cues), and appreciate the potential added financial value that eWOM
conveys. This translates into shoppers’engagement in price negotiation.
The current research has implications for theory, practice, and society. First, our
findings enhance our understanding of shopper dynamics in online shopping and
social commerce contexts. Social commerce is the employment of content generation
functionality in e-commerce, such that communication among potential and current
buyers is enhanced, and eWOM sharing regarding products and sellers is facilitated
(Hajli et al. 2017). As e-commerce platforms become more social (e.g., by including
more social features and capabilities, Huang and Benyoucef 2013), they have a greater
effect on buyer-seller interactions and the buying process. Past research indicates that
shopper review information affects shopper interaction with sellers (Xu et al. 2017).
248 S. LEVY AND Y. GVILI
The present research supports this findings, and demonstrates that the influence of
social commerce components (i.e., eWOM) goes beyond a direct effect on interaction.
It moderates the influence of consumer characteristics (i.e., consumer cultural values)
on their tendency to interact with the seller (i.e., negotiate).
In addition, research shows that cultural values are associated with negotiation
behaviour (Chuah, Hoffmann, and Larner 2014). The findings of the current research
show that the level of social commerce, which translates to various degrees of eWOM
sharing, moderates this relationship.
To the best of our knowledge, this is the first research to examine shoppers’price
negotiation behavior in a social commerce context, and specifically the impact of
eWOM on this behaviour.
This research also adds to the engagement literature by showing that engagement
is enhanced not only by tangible economic benefits offered by marketers (i.e. cou-
pons, discounts) (Edelman, Jaffe, and Kominers 2016; Pentina, Guilloux, and Micu
2018) but also by eWOM shared by others regarding the mere option to negotiate
price, where the benefit is uncertain.
This research contributes to consumer culture theory literature by demonstrating
that the influence of cultural differences on consumer behaviour may be mitigated by
external interventions (Bolton, Warlop, and Alba 2003; Nyer and Gopinath 2002). In
view of the increasing popularity of social media, eWOM is a feasible intervention that
interacts with cultural characteristics and affects shoppers’inclination to negotiate
Finally, our findings contribute to pricing theory in marketing by offering an explan-
ation for recent empirical results that show that fixed and flexible pricing policies
coexist in the same marketplace (Selcuk and Gokpinar 2018). Social media have facili-
tated eWOM communication among diverse cultures and ethnicities that are active in
the same market (Hanna, Rohm, and Crittenden 2011). Our findings show that engage-
ment in price negotiation varies by culture, hence the justification for these policies’
The current research has several practical implications. As global competition
increases, practitioners should note that the cultural diversity of international shoppers
may significantly affect the latters’inclination to negotiate prices. Hence, inviting shop-
pers to negotiate may not be equally effective across markets. Our findings also imply
that online sellers who facilitate eWOM or reviews concerning their invitations to
engage in counter-pricing will find such strategies to be more influential in collectivist
cultures and when their shoppers are highly involved in the transaction.
A social implication of this research relates to the effect of eWOM on enhancing
equality of opportunities across societies. Online shoppers from individualistic societies
are disadvantaged relative to collectivistic shoppers as they tend to refrain from using
valuable shared in-group information (Griffith, Yalcinkaya, and Rubera 2014). Our find-
ing suggest that individualists may refrain from using in-group information that can
lower their costs, possibly without being aware of the financial benefit they may be
missing. eWOM that is shared on social commerce platforms may help close this gap,
by acting as a social equalizer that provides all shoppers with equal opportunities to
take advantage of shoppers’common wisdom.
INTERNATIONAL JOURNAL OF ADVERTISING 249
The authors thank Yahel Bar-Shi and Chen Shreiber-Bezaleli for helping with the data collection.
No potential conflict of interest was reported by the authors.
Notes on contributors
Shalom Levy is a Senior Lecturer of Marketing at the Department of Economics and Business
Administration, Ariel University, Israel. Shalom holds a Ph.D. from Bar-Ilan University. Prior to
this, he worked as a media manager and head of planning and research in advertising compa-
nies. His work has been published in journals as Marketing Letters and International Journal of
Advertising and Journal of Advertising Research. Shalom Levy can be contacted at:
Yaniv Gvili is an assistant professor of marketing at the School of Business Administration of
Ono Academic College, Israel. Yaniv received his Ph.D. from Temple University. Prior to his aca-
demic career, he worked as an analyst and director of research in a global communications net-
work. Yaniv’s work has been published in leading journals including Journal of Advertising
Research, International Journal of Advertising, European Journal of marketing, and Psychology &
Marketing. Yaniv’s research interests include word of mouth, social networks, and new media
marketing. Email: firstname.lastname@example.org.
Abbas, R., and G. S. Mesch. 2015. Cultural values and facebook use among palestinian youth in
Israel. Computers in Human Behavior 48: 644–53.
Ackerman, D., and G. Tellis. 2001. Can culture affect prices? A cross-cultural study of shopping
and retail prices. Journal of Retailing 77: 57–82.
Amatulli, C., M. De Angelis, G. Pino, and G. Guido. 2017. Unsustainable luxury and negative
word-of-mouth: The role of shame and consumers’cultural orientation. In NA - advances in
consumer research, ed. Ayelet Gneezy, Vladas Griskevicius and Patti Williams. Vol. 45, 498-499.
Duluth, MN: Association for Consumer Research.
Araujo, T., P. Neijens, and R. Vliegenthart. 2017. Getting the word out on twitter: the role of
influentials, information brokers and strong ties in building word-of-mouth for brands.
International Journal of Advertising 36: 496–513.
Arnould, E.J., and C.J. Thompson. 2005. Consumer culture theory (CCT): twenty years of research.
Journal of Consumer Research 31: 868–82.
Askegaard, S., and D. Kjeldgaard. 2002. The water fish swim in? relations between culture and
marketing in the age of globalization. In Perspectives on marketing relationships, edited by
Thorbjørn Knudsen, Søren Askegaard and Niels Jørgensen, 13–35. Copenhagen: Thomson.
Barrutia, J.M., and M.P. Espinosa. 2014. Consumer expertise matters in price negotiation: an
empirical analysis of the determinants of mortgage loan prices in Spain prior to the financial
crisis. European Journal of Marketing 48: 1962–85.
Bauer, H.H., T. Falk, and M. Hammerschmidt. 2006. eTransQual: a transaction process-based
approach for capturing service quality in online shopping. Journal of Business Research 59:
Bolton, L.E., H.T. Keh, and J.W. Alba. 2010. How do price fairness perceptions differ across cul-
ture? Journal of Marketing Research 47: 564–76.
250 S. LEVY AND Y. GVILI
Bolton, L.E., L. Warlop, and J.W. Alba. 2003. Consumer perceptions of price (un) fairness. Journal
of Consumer Research 29: 474–91.
Boudette, N.E. 2017. "A Smartphone App to Relieve Your Car-Buying Agony," last modified
August 10, 2017, accessed July 14, 2018, https://www.nytimes.com/2017/08/10/automobiles/
Brett, J.M. 2007. Negotiating globally: How to negotiate deals, resolve disputes, and make decisions
across cultural boundaries. San Francisco, CA: Jossey-Bass.
Brodie, R.J., A. Ilic, B. Juric, and L. Hollebeek. 2013. Consumer engagement in a virtual Brand
community: an exploratory analysis. Journal of Business Research 66: 105–14.
Buchan, N.R., R.T. Croson, and E.J. Johnson. 2004. When do fair beliefs influence bargaining
behavior? experimental bargaining in japan and the United States. Journal of Consumer
Research 31: 181–90.
Bufete, T. 2016. "Facebook Marketplace: What You should Know." Consumer Reports, accessed
July 14, 2018, https://www.consumerreports.org/computers-internet/facebook-marketplace-
Bufete, T. 2017. "Haggling really Works when You Buy a New TV, Laptop, Or Other Device."
Consumer Reports, accessed July 14, 2018, https://www.consumerreports.org/saving-money/
Cai, D.A., S.R. Wilson, and L.E. Drake. 2000. Culture in the context of intercultural negotiation:
Individualism-collectivism and paths to integrative agreements. Human Communication
Research 26: 591–617.
Calder, B.J., and E.C. Malthouse. 2008. Media engagement and advertising effectiveness. In
Kellogg on advertising and media, edited by Bobby J. Calder, 1–36. Hoboken, NJ: Wiley.
Calder, B.J., E.C. Malthouse, and U. Schaedel. 2009. An experimental study of the relationship
between online engagement and advertising effectiveness. Journal of Interactive Marketing 23:
Carlson, J.P., J.W. Huppertz, and P.E. Neidermeyer. 2008. Price and consumer cost responsibility
effects on quality perceptions and price negotiation likelihood for healthcare services. Health
Marketing Quarterly 25: 303–28.
Chan, C.H., C. Cheng, and C. Hsu. 2007. Bargaining strategy formulation with CRM for an e-com-
merce agent. Electronic Commerce Research and Applications 6: 490–8.
Chandon, P., B. Wansink, and G. Laurent. 2000. A benefit congruency framework of sales promo-
tion effectiveness. Journal of Marketing 64: 65–81.
Chu, S., and J. Kim. 2018. The current state of knowledge on electronic word-of-mouth in adver-
tising research. International Journal of Advertising 37: 1–13.
Chuah, S., R. Hoffmann, and J. Larner. 2014. Chinese values and negotiation behaviour: a bar-
gaining experiment. International Business Review 23: 1203–11.
Colliander, J., M. Dahl
en, and E. Modig. 2015. Twitter for two: Investigating the effects of dia-
logue with customers in social media. International Journal of Advertising 34: 181–94.
Cortina, J.M., G. Chen, and W.P. Dunlap. 2001. Testing interaction effects in LISREL: Examination
and illustration of available procedures. Organizational Research Methods 4: 324–60.
Curty, R.G., and P. Zhang. 2013. Website features that gave rise to social commerce: a historical
analysis. Electronic Commerce Research and Applications 12: 260–79.
Dai, B., S. Forsythe, and W. Kwon. 2014. The impact of online shopping experience on risk per-
ceptions and online purchase intentions: Does product category matter? Journal of Electronic
Commerce Research 15: 13–24.
Davis, L., S. Wang, and A. Lindridge. 2008. Culture influences on emotional responses to on-line
store atmospheric cues. Journal of Business Research 61: 806–12.
De Kervenoael, R., A. Hallsworth, and J. Elms. 2014. Household pre-purchase practices and online
grocery shopping. Journal of Consumer Behaviour 13: 364–72.
De Mooij, M., and G. Hofstede. 2010. The hofstede model: Applications to global branding and
advertising strategy and research. International Journal of Advertising 29: 85–110.
De Pelsmacker, P., N. Dens, and A. Kolomiiets. 2018. The impact of text valence, star rating and
rated usefulness in online reviews. International Journal of Advertising 37: 340–59.
INTERNATIONAL JOURNAL OF ADVERTISING 251
De Veirman, M., V. Cauberghe, and L. Hudders. 2017. Marketing through instagram influencers:
the impact of number of followers and product divergence on Brand attitude. International
Journal of Advertising 36: 798–828.
Denegri-Knott, J., and M. Molesworth. 2010. Love it. buy it. sell it’consumer desire and the social
drama of eBay. Journal of Consumer Culture 10: 56–79.
Dholakia, U.M. 2001. A motivational process model of product involvement and consumer risk
perception. European Journal of Marketing 35: 1340–62.
Edelman, B., S. Jaffe, and S.D. Kominers. 2016. To groupon or not to groupon: the profitability of
deep discounts. Marketing Letters 27: 39–53.
Ekman, I., G. Chanel, S. J€
a, J.M. Kivikangas, M. Salminen, and N. Ravaja. 2012. Social inter-
action in games: Measuring physiological linkage and social presence. Simulation & Gaming
Evans, N.J., J. Phua, J. Lim, and H. Jun. 2017. Disclosing instagram influencer advertising: the
effects of disclosure language on advertising recognition, attitudes, and behavioral intent.
Journal of Interactive Advertising 17: 138–49.
Facebook. 2018. "Marketplace: Get what You Want Near You.", accessed July 14, 2018, https://
Fang, T. 2006. Negotiation: the chinese style. Journal of Business & Industrial Marketing 21:
Fu, P., C. Wu, and Y. Cho. 2017. What makes users share content on facebook? compatibility
among psychological incentive, social Capital focus, and content type. Computers in Human
Behavior 67: 23–32.
Gavilanes, J.M., T.C. Flatten, and M. Brettel. 2018. Content strategies for digital consumer
engagement in social networks: Why advertising is an antecedent of engagement. Journal of
Advertising 47: 4–23.
Geertz, C. 2008. Local knowledge: Further essays in interpretive anthropology. New York: Basic
Gillison, S.T., W.M. Northington, and S.E. Beatty. 2014. Understanding customer bargaining in
retail stores: a customer perspective. Journal of Marketing Theory and Practice 22: 151–68.
Graham, J.L., A.T. Mintu, and W. Rodgers. 1994. Explorations of negotiation behaviors in ten for-
eign cultures using a model developed in the United States. Management Science 40: 72–95.
Grau, S.L., and Y.C. Zotos. 2016. Gender stereotypes in advertising: a review of current research.
International Journal of Advertising 35: 761–70.
Griffith, D.A., G. Yalcinkaya, and G. Rubera. 2014. Country-level performance of new experience
products in a global rollout: the moderating effects of economic wealth and national culture.
Journal of International Marketing 22: 1–20.
Gvili, Y., and S. Levy. 2018. Consumer engagement with eWOM on social media: the role of
social Capital. Online Information Review 42: 482–505.
Hair, J.F., W.C. Black, B.J. Babin, and R.E. Anderson. 2010. Multivariate data analysis. Vol. 7.
Englewood Cliffs, NJ: Prentice Hall.
Hajli, N., J. Sims, A.H. Zadeh, and M. Richard. 2017. A social commerce investigation of the role
of trust in a social networking site on purchase intentions. Journal of Business Research 71:
Han, M.C., and Y. Kim. 2017. Why consumers hesitate to shop online: Perceived risk and product
involvement on taobao.com. Journal of Promotion Management 23: 24–44.
Hanna, R., A. Rohm, and V.L. Crittenden. 2011. We’re all connected: the power of the social
media ecosystem. Business Horizons 54: 265–73.
Hartmann, P., V. Apaolaza, C. D’Souza, J.M. Barrutia, and C. Echebarria. 2014. Environmental
threat appeals in green advertising: the role of fear arousal and coping efficacy. International
Journal of Advertising 33: 741–65.
Hasker, K., and R. Sickles. 2010. eBay in the economic literature: Analysis of an auction market-
place. Review of Industrial Organization 37: 3–42.
Hayes, A.F. 2013. Introduction to mediation, moderation, and conditional process analysis:
Methodology in the social sciences. New York: Guilford Press.
252 S. LEVY AND Y. GVILI
Hayes, J.L., Y. Shan, and K.W. King. 2018. The interconnected role of strength of Brand and inter-
personal relationships and user comment valence on Brand video sharing behaviour.
International Journal of Advertising 37: 142–64.
Hemetsberger, A. 2003. When consumers produce on the internet: the relationship between
cognitive-affective, socially-based, and behavioral involvement of prosumers. The Journal of
Social Psychology 12: 1–20.
Hennig-Thurau, T., K.P. Gwinner, G. Walsh, and D.D. Gremler. 2004. Electronic word-of-mouth via
consumer-opinion platforms: What motivates consumers to articulate themselves on the inter-
net?. Journal of Interactive Marketing 18: 38–52.
andez, B., J. Jim
enez, and M.J. Mart
ın. 2010. Customer behavior in electronic commerce: the
moderating effect of e-purchasing experience. Journal of Business Research 63: 964–71.
Herrmann, G.M. 2003. Negotiating culture: Conflict and consensus in US garage-sale bargaining.
Ethnology 22: 237–52.
Hofstede Center. 2018. "Individualism Scores.", accessed July 31, 2018, https://www.hofstede-
Hofstede, G., G. Hofstede, and M. Minkov. 2010. Cultures and organizations: Software of the mind,
and McGraw-Hill USA. 3rd ed. New York: McGraw-Hill.
Hofstede, G. 1991. Cultures and organizations: Software of the mind (London and New York,
McGraw Hill). London: McGraw-Hill.
Hofstede, G. 2001. Culture’s consequences: Comparing values, behaviors, institutions and organiza-
tions across nations. 2nd ed. Thousand Oaks, CA: Sage.
Hollebeek, L. 2011. Exploring customer Brand engagement: Definition and themes. Journal of
Strategic Marketing 19: 555–73.
Holmes, Y.M., L.S. Beitelspacher, B. Hochstein, and W. Bolander. 2017. Let’s make a deal:”price
outcomes and the interaction of customer persuasion knowledge and salesperson negotiation
strategies. Journal of Business Research 78: 81–92.
Holt, D.B. 1997. Poststructuralist lifestyle analysis: Conceptualizing the social patterning of con-
sumption in postmodernity. Journal of Consumer Research 23: 326–50.
Hong, J.W., A. Muderrisoglu, and G.M. Zinkhan. 1987. Cultural differences and advertising expres-
sion: a comparative content analysis of Japanese and US magazine advertising. Journal of
Advertising 16: 55–68.
Huang, Z., and M. Benyoucef. 2013. From e-commerce to social commerce: a close look at
design features. Electronic Commerce Research and Applications 12: 246–59.
Huff, L., and L. Kelley. 2005. Is collectivism a liability? The impact of culture on organizational
trust and customer orientation: a seven-nation study. Journal of Business Research 58: 96–102.
Khalifa, M., and V. Liu. 2007. Online consumer retention: Contingent effects of online shopping
habit and online shopping experience. European Journal of Information Systems 16: 780–92.
Kim, D.H., Y. Sung, and M. Drumwright. 2018. Where I come from’determines,‘How I construe
my future’: the fit effect of culture, temporal distance, and construal level. International
Journal of Advertising 37: 270–88.
Ladhari, R. 2007. The effect of consumption emotions on satisfaction and word-of-mouth com-
munications. Psychology and Marketing 24: 1085–108.
ere, B., H. Joosten, E.C. Malthouse, M. Van Birgelen, P. Aksoy, W.H. Kunz, and M. Huang.
2013. Value fusion: the blending of consumer and firm value in the distinct context of mobile
technologies and social media. Journal of Service Management 24: 268–93.
Lashinsky, A. 2017. "How Alibaba’s Jack Ma is Building a Truly Global Retail Empire", last modi-
fied March 24, 2017, accessed July 10, 2018, http://fortune.com/2017/03/24/jack-ma-alibaba-
Laurent, G., and J. Kapferer. 1985. Measuring consumer involvement profiles. Journal of
Marketing Research 22: 41–53.
Lee, D.Y. 2000. Retail bargaining behaviour of American and Chinese customers. European
Journal of Marketing 34: 190–206.
Lichtenstein, D.R., R.G. Netemeyer, and S. Burton. 1995. Assessing the domain specificity of deal
proneness: A field study. Journal of Consumer Research 22: 314–26.
INTERNATIONAL JOURNAL OF ADVERTISING 253
Magee, J.C., A.D. Galinsky, and D.H. Gruenfeld. 2007. Power, propensity to negotiate, and moving
first in competitive interactions. Personality and Social Psychology Bulletin 33: 200–12.
Minkov, M., and G. Hofstede. 2011. The evolution of hofstede’s doctrine. Cross Cultural
Management: An International Journal 18: 10–20.
Mintu-Wimsatt, A., and R.J. Calantone. 1996. Exploring factors that affect negotiators’problem-
solving orientation. Journal of Business & Industrial Marketing 11: 61–73.
Mittal, B., and M. Lee. 1989. A causal model of consumer involvement. Journal of Economic
Psychology 10: 363–89.
Moon, J.H., E. Kim, S.M. Choi, and Y. Sung. 2013. Keep the social in social media: the role of
social interaction in avatar-based virtual shopping. Journal of Interactive Advertising 13: 14–26.
Moon, J., D. Chadee, and S. Tikoo. 2008. Culture, product type, and price influences on con-
sumer purchase intention to buy personalized products online. Journal of Business Research
Nyer, P.U., and M. Gopinath. 2002. Bargaining behavior and acculturation: a cross-cultural investi-
gation. Journal of International Consumer Marketing 14: 101–22.
Pentina, I., V. Guilloux, and A.C. Micu. 2018. Exploring social media engagement behaviors in the
context of luxury brands. Journal of Advertising 47: 55–69.
Pergelova, A., and F. Angulo-Ruiz. 2017. Comparing advertising effectiveness in South-american
and North-American contexts: Testing hofstede’s and inglehart’s cultural dimensions in the
higher education sector. International Journal of Advertising 36: 870–92.
Petty, R.E., J.T. Cacioppo, and D. Schumann. 1983. Central and peripheral routes to advertising
effectiveness: the moderating role of involvement. Journal of Consumer Research 10: 135–46.
Pizam, A., and G. Jeong. 1996. Cross-cultural tourist behavior: Perceptions of korean tour-guides.
Tourism Management 17: 277–86.
Porges, S. 2016. "Dear would-be Airbnb Guests: Here’s Why Hosts Keep Turning You Down.", last
modified January 18, 2016, accessed July 14, 2018, https://www.forbes.com/sites/sethporges/
Prahalad, C.K., and V. Ramaswamy. 2004. Co-creation experiences: the next practice in value cre-
ation. Journal of Interactive Marketing 18: 5–14.
Rampen, J. 2016. "The eBay Sellers Making Huge Profits from Your Mistakes - these are their
Secrets.", last modified August 15, 2016, accessed July 14, 2018, https://www.mirror.co.uk/
Rappaport, S.D. 2010. Listening solutions: a marketer’s guide to software and services. Journal of
Advertising Research 50: 197–2013.
Reif, J.A., and F.C. Brodbeck. 2017. When do people initiate a negotiation? The role of discrep-
ancy, satisfaction, and ability beliefs. Negotiation and Conflict Management Research 10: 46–66.
Rossmann, A., A. Rossmann, K.R. Ranjan, K.R. Ranjan, P. Sugathan, and P. Sugathan. 2016. Drivers
of user engagement in eWoM communication. Journal of Services Marketing 30: 541–53.
Ruvio, A. 2008. Unique like everybody else? The dual role of consumers’need for uniqueness.
Psychology and Marketing 25: 444–64.
Ruvio, A., and A. Shoham. 2007. Innovativeness, exploratory behavior, market mavenship, and
opinion leadership: an empirical examination in the asian context. Psychology and Marketing
Schivinski, B., G. Christodoulides, and D. Dabrowski. 2016. Measuring consumers’engagement
with Brand-related social-media content: Development and validation of a scale that identifies
levels of social-media engagement with brands. Journal of Advertising Research 56: 64–80.
Schultz, D.E., and J. Peltier. 2013. Social media’s slippery slope: Challenges, opportunities and
future research directions. Journal of Research in Interactive Marketing 7: 86–99.
Selcuk, C., and B. Gokpinar. 2018. Fixed vs. flexible pricing in a competitive market. Management
Science. 64: 5584–5598.
Sharma, V.M., and K.S. Krishnan. 2001. Recognizing the importance of consumer bargaining:
Strategic marketing implications. Journal of Marketing Theory and Practice 9: 24–37.
254 S. LEVY AND Y. GVILI
Shin, D., D. Shin, M. Choi, M. Choi, J. Hyun Kim, J. Hyun Kim, J. Lee, and J. Lee. 2016. Interaction,
engagement, and perceived interactivity in single-handed interaction. Internet Research 26:
Simonin, B.L., and J.A. Ruth. 1998. Is a company known by the company it keeps? assessing the
spillover effects of Brand alliances on consumer Brand attitudes. Journal of Marketing Research
Stafford, M.R., and B. Stern. 2002. Consumer bidding behavior on internet auction sites.
International Journal of Electronic Commerce 7: 135–50.
Standifird, S.S., M.R. Roelofs, and Y. Durham. 2005. The impact of eBay’s buy-it-now function on
bidder behavior. International Journal of Electronic Commerce 9: 167–76.
Stout, H. 2013. "More Retailers See Haggling as a Price of Doing Business.", last modified
December 15, 2013, accessed July 8, 2018. https://www.nytimes.com/2013/12/16/business/
Sun, H., W. Ni, and Z. Wang. 2016. A consumption system model integrating quality, satisfaction
and behavioral intentions in online shopping. Information Technology and Management 17:
Taras, V., B.L. Kirkman, and P. Steel. 2010. Examining the impact of culture’s consequences: a
three-decade, multilevel, Meta-analytic review of hofstede’s cultural value dimensions. Journal
of Applied Psychology 95: 405–39.
Thompson, C.J., and E.C. Hirschman. 1995. Understanding the socialized body: a poststructuralist
analysis of consumers’self-conceptions, body images, and self-care practices. Journal of
Consumer Research 22: 139–53.
Triandis, H.C. 1990. Cross-cultural studies of individualism and collectivism. In Nebraska sympo-
sium on motivation, edited by J. Berman. Lincoln: University of Nebraska Press.
Triandis, H.C. 2001. Individualism-collectivism and personality. Journal of Personality 69: 907–24.
Tsai, W.S., and L.R. Men. 2017. Consumer engagement with brands on social network sites: a
cross-cultural comparison of china and the USA. Journal of Marketing Communications 23:
Van Doorn, J., K.N. Lemon, V. Mittal, S. Nass, D. Pick, P. Pirner, and P.C. Verhoef. 2010. Customer
engagement behavior: Theoretical foundations and research directions. Journal of Service
Research 13: 253–66.
Van Hoorn, A. 2015. Individualist–collectivist culture and trust radius: a multilevel approach.
Journal of Cross-Cultural Psychology 46: 269–76.
Vanian, J. 2018. "Amazon has Over 100 Million Prime Members.", last modified April 19, 2018,
accessed July 10, 2018, http://fortune.com/2018/04/18/amazon-prime-members-millions/.
Vermeir, I., and P. Van Kenhove. 2005. The influence of need for closure and perceived time
pressure on search effort for price and promotional information in a grocery shopping con-
text. Psychology and Marketing 22: 71–95.
Voorveld, H.A., P.C. Neijens, and E.G. Smit. 2009. Consumers’responses to Brand websites: an
interdisciplinary review. Internet Research 19: 535–65.
Voorveld, H.A., G. van Noort, D.G. Muntinga, and F. Bronner. 2018. Engagement with social
media and social media advertising: the differentiating role of platform type. Journal of
Advertising 47: 38–54.
Watkins, H.S., and R. Liu. 1996. Collectivism, individualism and in-group membership:
Implications for consumer complaining behaviors in multicultural contexts. Journal of
International Consumer Marketing 8: 69–96.
Weinswig, D. 2017. "A Global View of Amazon Prime Day, from East to West.", last modified July
12, 2017, accessed July 10, 2018, https://www.forbes.com/sites/deborahweinswig/2017/07/12/
Wu, T., D. Scott, and C. Yang. 2013. Advanced or addicted? Exploring the relationship of recre-
ation specialization to flow experiences and online game addiction. Leisure Sciences 35:
INTERNATIONAL JOURNAL OF ADVERTISING 255
Xu, X., Q. Li, L. Peng, T. Hsia, C. Huang, and J. Wu. 2017. The impact of informational incentives
and social influence on consumer behavior during alibaba’s online shopping carnival.
Computers in Human Behavior 76: 245–54.
Yoo, B., and N. Donthu. 2005. The effect of personal cultural orientation on consumer ethnocen-
trism: Evaluations and behaviors of US consumers toward japanese products. Journal of
International Consumer Marketing 18: 7–44.
Yoo, B., N. Donthu, and T. Lenartowicz. 2011. Measuring hofstede’s five dimensions of cultural
values at the individual level: Development and validation of CVSCALE. Journal of
International Consumer Marketing 23: 193–210.
Zaichkowsky, J.L. 1985. Measuring the involvement construct. Journal of Consumer Research 12:
Zeng, X., S. Dasgupta, and C.B. Weinberg. 2012. How good are you at getting a lower price? A
field study of the US automobile market. Journal of Consumer Policy 35: 255–74.
Zhang, X., and B. Jiang. 2014. Increasing price transparency: Implications of consumer price post-
ing for consumers’haggling behavior and a seller’s pricing strategies. Journal of Interactive
Marketing 28: 68–85.
256 S. LEVY AND Y. GVILI
Mock eBay product pages used in Study 2:
INTERNATIONAL JOURNAL OF ADVERTISING 257