This products are selected twice as often if they


project will analyze the impact of good vs. bad ratings during the first stage
of the decision-making process when booking a hotel. It will test the link
between numerical ratings given to a product or service and the number of
verbal reviews it has received while controlling subject susceptibility to
interpersonal influence. A full factorial between subject’s design of 2 levels
of ratings (good vs. bad) x 2 levels of reviews (high vs. low) in a
decision-controlled setting will be conducted. Results till now suggest an
asymmetric interaction between numerical ratings and reviews: if the rating is
bad, the number of reviews have no effect on how trustworthy the rating is, but
conversely, when the rating is good, the trust in the rating depends on the
number of reviews. Academic and managerial implications of this study and scope
for future research have also been discussed.



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we are relying more and more on the aggregated opinions of peers online, contributions
made by users on technological platforms facilitate the interaction between
like-minded community members who share shopping interests, thus facilitating
the decision-making process (Amblee, 2014). These contributions have become the
main source of social influence when making a purchase (Anderson,

Within such a technological context, companies in the consumer sector – tourism
and hospitality, travel, leisure, electrical devices, etc – must face the
challenge of managing the large scale, anonymous and brief opinions of others.

Therefore, organizations need new knowledge that allows them to capture,
analyse, interpret and manage online social influence (Sinan Aral, 2012) (NAVEEN AMBLEE, 2014).


literature recognizes that consumers have the ability to influence each other.

On the Internet, this influence is omnipresent and is exerted through, among
other things, recommendations, numerical ratings and verbal reviews. Previous
research has focused on the influence that online recommendations and reviews
have on the different stages of the decision-making process when purchasing a
product. Research has revealed that products are selected twice as often if
they are recommended by others and this influence is dependent on the type of
recommendation source. Online recommendation systems offered by online
retailers are more influential than the recommendation from experts or other consumers.

These results are moderated by the type of product. With regard to the reviews,
its influence on buying decisions has been studied for different type of
products: books, hotel stays, in terms of both sought-after and experiential
goods, and also the ability of comments to modify the visibility of a product.

Reviews have also shown to act as anchors of consumer experience and to
encourage subsequent reviews on the Net (Smith, 2011).


online consumers have to deal with huge amount of information, new search
engines, different devices, and new strategies to approach information in order
to make a purchasing decision. In this new context, online ratings become one
of the most trusted sources when making e-commerce decisions. Usually,
consumers have faith in these ratings and view them as trustworthy. A Nielsen
report found that consumers’ ratings were the second most-trusted source of
brand information (after recommendations from friends and family). Companies
are sensitive to these changes. (Lipsman, 2007) examined the impact of
consumer-generated reviews on the price consumers were willing to pay for a
service to be delivered offline. Consumers were willing to pay at least 20
percent more for services which have received an “Excellent,” or 5-star rating
than if the same service has received a “Good,” or 4-star rating. Despite the
influence of the interest in ratings, only few researchers have so far analyzed
the influence exerted by anonymous and non-expert raters on consumer purchasing
decisions. Moreover, in online purchasing decisions, people usually receive two
types of information simultaneously: an overall numerical rating and a sample
of individual verbal reviews. Both exert a particular influence on the
consumers, and their interaction is particularly telling. No research we are
aware of, however, has investigated the interaction between the influence of
ratings and the volume of reviews on consumers’ purchasing decisions.

Therefore, the goal of this paper is to deepen the knowledge about the
influence of ratings and number of reviews. Specifically, we look at the
interaction between the rating and the number of reviews that goes along with
it, in decisions taken during the first stage of the purchasing decision
process. We will analyze the mediating effect of trust on the relationship
between the rating and the intention to shortlist a product or service, as well
as the moderating role of the number of reviews in the indirect effect of the
numerical rating on trustworthiness. From a business perspective, gaining a
better understanding of how product ratings and reviews influence consumer
choice is vital to further understand the relationship between online customer
reviews and business performance. (Diana Gavilan, 2017)


Booking intent and perceptions of trust

There is wide agreement (sciencedirect, 2010) that with the
advance of technology (especially the Internet) the information sources
available to prospective consumers have grown. For many consumers of tourism or
hospitality product a review of what is being ‘said’ in cyber space forms part
of the information collection process when selecting a product. This means
there is a growing need to understand how various elements of online
information search and review influence consumer behaviour (Seggers, 2009) especially the propensity to book a
hotel room. Related to willingness to book is whether or not a potential
consumer forms a view that the hotel can be trusted. (Sichtmann, 2007)  found that trust in a firm positively affects
purchase intentions. As previous researchers (e.g. Sichtmann,
2007) note,
marketers often want to reduce potential consumer uncertainly associated with
purchasing a product. To do so firms often attempt to build trust in their

(Sirdeshmukh, 2002) defines consumer
trust as the expectation that a firm is dependable and will deliver on its
promises. (Wang, 2005) reviewed
the concept of trust in the online purchase space used by companies selling goods
or services. They argue that trust is one of the most important factors in
determining whether people will purchase online. While trust can be influenced
by the broader context such as the industry itself or by firm level website
design features, it is often the actions of the frontline employee and the firm
itself which has the most impact on building trust (Grayson, 2008). Consumer satisfaction in previous
interactions with frontline service staff influences cognitive trust, which is
consumer confidence or willingness to trust the service provider in the future (Johnson, 2005). Consumer reviews,
found on travel and hospitality online communities, provide customers with
vicarious access to prior service experience on which they can base their
belief or trust that a firm will deliver quality service. (Chen, 2008) also argues
that potential consumers use online consumer reviews as one way to reduce risk
and uncertainty in the purchase situation. The reviews and recommendations of
other customers can assist in determining whether to trust the hotel under
consideration. This study investigates how a range of factors could be causally
linked to two key evaluations: likelihood of purchase and trust in the target
entity. As mentioned, there is a range of potential influencing factors but
some that are of practical and theoretical importance include the content or
target of reviews, the overall tone or valence of the reviews (as a
collection), the framing of the review set (what is read first) and easy-to-process
peripheral information such as consumer generated numerical ratings.


Outcomes of Trust

reputation, perceived size, and trust are beliefs that the consumer has formed
on the basis of information that the consumer has about the merchant. According
to the Theory of Reasoned Action (Fishbean, 1985) and the Theory of
Planned Behavior (Azten, 1985) beliefs affect the
person’s attitudes; that is, their favorable or unfavorable evaluations of the
merchant and the site. The theory asserts that attitudes in turn influence
behavioral intention, which is a good predictor of actual behavior (i.e.,
actual purchase). See, for example, (Driver, 1992), (Notani, 1997)for demonstrations of
the good predictive validity of intentions on actual purchases when consumers
are under volitional control.

consumer’s willingness to buy from an Internet seller (i.e., behavioral
intention) is contingent on the consumer’s attitude towards the store, which,
in turn, is affected by the seller’s ability to evoke consumers’ trust (i.e.,
belief). Consumers are less likely to patronize stores that fail to create a
sense of trustworthiness. Higher trust, on the other hand, will not only
directly improve attitudes towards a store, but might also have an influence
indirectly by way of reducing the perceived level of risk associated with
buying from that particular store. Besides helping to shape attitudes,
perceived risk might also have an independent, direct influence on the
willingness to buy. A consumer may be willing to buy from an Internet store
which is perceived as low risk, even if the consumer’s attitudes towards that
merchant are not highly positive. Conversely, a consumer may not be willing to
buy from a merchant perceived as being high risk, even in the presence of
positive attitudes towards that merchant. The direct influence of perceived
risk on intention is related to the notion of perceived behavioral control in
the theory of planned behavior (Ajzen, 1991). Individuals are
likely to hold beliefs of high personal control, when they feel that successful
shopping experience is up to them. The perceived risk associated with shopping
in the store may reduce the consumer’s perception of behavioral control, and
the extent to which this occurs might negatively influence willingness to shop.