This 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. IntroductionAs 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, 1998). 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). Marketing 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). Today 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 trustThere 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 product.(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.
We now draw upon research in industrial marketing. There is a scarcity of research on trust in consumer marketing (Geyskens, onlinelibrary.wiley, 1998). A meta-analysis of empirical research on antecedents and consequences of trust in marketing relationships found no significant differences in results between industrial and consumer categories of merchandise (Geyskens, 1997).
In industrial marketing, the most salient source of a buyer’s trust in the merchant organization is the salesperson; trust in the salesperson is dependent on the salesperson’s expertise, likability, and similarity to the customer (Doney, 1997). In the Internet context, the salesperson is replaced by a website (Lohse, 1998). The web site mediates the relationship between the consumer and the merchant organization.
Sales organizations have been found to create trust in the customer by demonstrating that they consider their customers’ interests and are willing to make short-term sacrifices. These sacrifices convey that the merchant is not purely driven by a selfish short-term profit motive (Ganesan, 1997) Trust and RiskTrust and risk are closely interrelated (Mayer, onlinelibrary.wiley, 1995)Trust is a social lubricant that allows consumers to transact with merchants who are not part of their immediate network. Trust in a merchant mitigates the consumer’s perception of the risks involved in a purchase situation. The higher the initial perceptions of risk, the higher the trust needed to facilitate a transaction.Risk is defined as a consumer’s perceptions of the uncertainty and adverse consequences of engaging in an activity (Dowling, 1994)The Internet is an open, global, heterogeneous, and constantly changing marketing channel.
Moreover, the channel makes it hard to inspect physical goods. There seems little assurance that the customer will get what he or shees see on the computer screen, in the quantity ordered. If problems arise, sellers can always blame technical problems that are beyond their control. Many sites do not even confirm the order, let alone stay in touch with the shopper until the merchandise has been received or consumed. Because of the newness of the channel, many consumers may be uncertain about the hazards at present and their full consequences. All these reasons increase the uncertainty, and possibly the perceived riskiness of shopping on the Internet. When risk is present, trust is needed to make transactions possible. That is, consumer trust toward a merchant reduces the perceived riskiness of a specific webstore.
Outcomes of TrustPerceived 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.A 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.