Thisproject will analyze the impact of good vs. bad ratings during the first stageof the decision-making process when booking a hotel. It will test the linkbetween numerical ratings given to a product or service and the number ofverbal reviews it has received while controlling subject susceptibility tointerpersonal influence.
A full factorial between subject’s design of 2 levelsof ratings (good vs. bad) x 2 levels of reviews (high vs. low) in adecision-controlled setting will be conducted. Results till now suggest anasymmetric interaction between numerical ratings and reviews: if the rating isbad, the number of reviews have no effect on how trustworthy the rating is, butconversely, when the rating is good, the trust in the rating depends on thenumber of reviews.
Academic and managerial implications of this study and scopefor future research have also been discussed. Introduction Aswe are relying more and more on the aggregated opinions of peers online, contributionsmade by users on technological platforms facilitate the interaction betweenlike-minded community members who share shopping interests, thus facilitatingthe decision-making process (Amblee, 2014). These contributions have become themain source of social influence when making a purchase (Anderson, 1998).Within such a technological context, companies in the consumer sector – tourismand hospitality, travel, leisure, electrical devices, etc – must face thechallenge 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). Marketingliterature recognizes that consumers have the ability to influence each other.
On the Internet, this influence is omnipresent and is exerted through, amongother things, recommendations, numerical ratings and verbal reviews. Previousresearch has focused on the influence that online recommendations and reviewshave on the different stages of the decision-making process when purchasing aproduct. Research has revealed that products are selected twice as often ifthey are recommended by others and this influence is dependent on the type ofrecommendation source. Online recommendation systems offered by onlineretailers 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 ofproducts: books, hotel stays, in terms of both sought-after and experientialgoods, 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 toencourage subsequent reviews on the Net (Smith, 2011). Todayonline consumers have to deal with huge amount of information, new searchengines, different devices, and new strategies to approach information in orderto make a purchasing decision. In this new context, online ratings become oneof the most trusted sources when making e-commerce decisions. Usually,consumers have faith in these ratings and view them as trustworthy.
A Nielsenreport found that consumers’ ratings were the second most-trusted source ofbrand information (after recommendations from friends and family). Companiesare sensitive to these changes. (Lipsman, 2007) examined the impact ofconsumer-generated reviews on the price consumers were willing to pay for aservice to be delivered offline. Consumers were willing to pay at least 20percent more for services which have received an “Excellent,” or 5-star ratingthan if the same service has received a “Good,” or 4-star rating. Despite theinfluence of the interest in ratings, only few researchers have so far analyzedthe influence exerted by anonymous and non-expert raters on consumer purchasingdecisions. Moreover, in online purchasing decisions, people usually receive twotypes of information simultaneously: an overall numerical rating and a sampleof individual verbal reviews.
Both exert a particular influence on theconsumers, and their interaction is particularly telling. No research we areaware of, however, has investigated the interaction between the influence ofratings and the volume of reviews on consumers’ purchasing decisions.Therefore, the goal of this paper is to deepen the knowledge about theinfluence of ratings and number of reviews.
Specifically, we look at theinteraction between the rating and the number of reviews that goes along withit, in decisions taken during the first stage of the purchasing decisionprocess. We will analyze the mediating effect of trust on the relationshipbetween the rating and the intention to shortlist a product or service, as wellas the moderating role of the number of reviews in the indirect effect of thenumerical rating on trustworthiness. From a business perspective, gaining abetter understanding of how product ratings and reviews influence consumerchoice is vital to further understand the relationship between online customerreviews and business performance. (Diana Gavilan, 2017) Booking intent and perceptions of trustThere is wide agreement (sciencedirect, 2010) that with theadvance of technology (especially the Internet) the information sourcesavailable to prospective consumers have grown. For many consumers of tourism orhospitality product a review of what is being ‘said’ in cyber space forms partof the information collection process when selecting a product. This meansthere is a growing need to understand how various elements of onlineinformation search and review influence consumer behaviour (Seggers, 2009) especially the propensity to book ahotel room. Related to willingness to book is whether or not a potentialconsumer forms a view that the hotel can be trusted. (Sichtmann, 2007) found that trust in a firm positively affectspurchase intentions.
As previous researchers (e.g. Sichtmann,2007) note,marketers often want to reduce potential consumer uncertainly associated withpurchasing a product. To do so firms often attempt to build trust in theirproduct.
(Sirdeshmukh, 2002) defines consumertrust as the expectation that a firm is dependable and will deliver on itspromises. (Wang, 2005) reviewedthe concept of trust in the online purchase space used by companies selling goodsor services. They argue that trust is one of the most important factors indetermining whether people will purchase online. While trust can be influencedby the broader context such as the industry itself or by firm level websitedesign features, it is often the actions of the frontline employee and the firmitself which has the most impact on building trust (Grayson, 2008).
Consumer satisfaction in previousinteractions with frontline service staff influences cognitive trust, which isconsumer confidence or willingness to trust the service provider in the future (Johnson, 2005). Consumer reviews,found on travel and hospitality online communities, provide customers withvicarious access to prior service experience on which they can base theirbelief or trust that a firm will deliver quality service. (Chen, 2008) also arguesthat potential consumers use online consumer reviews as one way to reduce riskand uncertainty in the purchase situation. The reviews and recommendations ofother customers can assist in determining whether to trust the hotel underconsideration. This study investigates how a range of factors could be causallylinked to two key evaluations: likelihood of purchase and trust in the targetentity.
As mentioned, there is a range of potential influencing factors butsome that are of practical and theoretical importance include the content ortarget of reviews, the overall tone or valence of the reviews (as acollection), the framing of the review set (what is read first) and easy-to-processperipheral information such as consumer generated numerical ratings. Outcomes of TrustPerceivedreputation, perceived size, and trust are beliefs that the consumer has formedon the basis of information that the consumer has about the merchant. Accordingto the Theory of Reasoned Action (Fishbean, 1985) and the Theory ofPlanned Behavior (Azten, 1985) beliefs affect theperson’s attitudes; that is, their favorable or unfavorable evaluations of themerchant and the site. The theory asserts that attitudes in turn influencebehavioral intention, which is a good predictor of actual behavior (i.
e.,actual purchase). See, for example, (Driver, 1992), (Notani, 1997)for demonstrations ofthe good predictive validity of intentions on actual purchases when consumersare under volitional control.Aconsumer’s willingness to buy from an Internet seller (i.e., behavioralintention) 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 asense of trustworthiness. Higher trust, on the other hand, will not onlydirectly improve attitudes towards a store, but might also have an influenceindirectly by way of reducing the perceived level of risk associated withbuying from that particular store. Besides helping to shape attitudes,perceived risk might also have an independent, direct influence on thewillingness to buy.
A consumer may be willing to buy from an Internet storewhich is perceived as low risk, even if the consumer’s attitudes towards thatmerchant are not highly positive. Conversely, a consumer may not be willing tobuy from a merchant perceived as being high risk, even in the presence ofpositive attitudes towards that merchant. The direct influence of perceivedrisk on intention is related to the notion of perceived behavioral control inthe theory of planned behavior (Ajzen, 1991).
Individuals arelikely to hold beliefs of high personal control, when they feel that successfulshopping experience is up to them. The perceived risk associated with shoppingin the store may reduce the consumer’s perception of behavioral control, andthe extent to which this occurs might negatively influence willingness to shop.