The Impact of Restaurant Reviews on Customer Decisions

 Restaurant Review Systems Context
As of January 2013 Yelp recorded 100 million visits on Yelp. om not including the 9. 4 million unique users of its mobile application, ranking Yelp the 34th most trafficked website in the US. (Wilhelm, 2013). In addition, The Zagat New York guide sold 500,000 copies last year and it now includes 2,050 restaurants from all five boroughs in its 2013 edition. (Talmadge, 2008 ) Ultimately, The Guide Rouge sells around 1. 2 million copies per year in eight countries, and it impressively sold out 120,000 copies in no more than three days, on its first Tokyo 2008 edition (Michelin, 2011).
Davis (2009) synthesizes “Restaurant reviews which, in addition to recording eating experiences, educate and inform us about how to culturally contextualize, judge, and compare eating experiences in both explicit and implicit ways, how to expand our vocabulary and fill up the bank, reviews are an important locus of meaning in the realm of food. ” Coherently, gourmets argue that with the absence of writing, food is merely confined to its biological purpose and economic standing. Recording the dining experiences, avoids the quotidian encapsulation and impulses food discourse into the realm of intellectual pursuit. (Davis, 2009 , pp. 13-16)

Food, being a vital necessity of human nature, has developed in the last centuries as not only a survival instinct, but a desire that can drive customers to a satisfactory and rewarding emotional experience. (Berridge, 2001 , pp. 234-242) Hence, the advancing phenomenon of eating out and the fast growing pace of the Gastronomic industry, has gained uncountable followers. (Upadhyay, Singh, & Thomas, 2007) The purpose of this study is to explore the influence of restaurant reviews upon consumer selecting criteria. Examine the information quality, and source credibility of restaurant review systems and their influence on consumer’s utilization.
Overview of the themes Technological advances have brought the ease of accessibility to immeasurable information. Restaurant reviews systems are widely spread, due to the fact that consumers are willing to refer to either expert or peer created reviews before a culinary venture, to avoid potential risk or uncertainty over food/service quality. (Choi & Ok). In contrast, Bouton and Kirchsteiger (2001), elaborate on the theory that the existence of favourable rankings might affect consumers by increasing the market power of firms, leading to inflating flexible prices and therefore lowering customers’ solvency power. Bouton & Kirchsteiger, 2011)
Peer vs. Expert Opinion Constraints Luca (2011) discusses the criticisms on the reliability of the information obtained from both expert and en masse review systems. Constraints such as the hedonic value of palatability, as a result of the diverse interpretations of quality perception in conjunction with the possibility of stakeholders altering submissions, that will cause biased results. Equally important, the subjectivity of information on peer reviewed evaluations, which normally reflect a non representative sample of customers. (Luca, 2011)
Concerns in the case of expert reviews, for example the Michelin Guide, include the propensity to cover small segments of a market and the companies’ obligation to comply with mandatory disclosure laws. (Luca, 2011) Furthermore, Geraud et al. (2012) considerate that even expert reviews might be somehow biased; bolstering French cuisine. Notwithstanding, Johnson et al. (2005) attributed the hegemony Francoise, to the long tradition and paramount magnitude of haute cuisine culture in France. Existing literature demonstrates the significance of experts? opinion and social learning, to model consumer criteria.
However the Michelin star system, especially in Europe, is to some extent overwhelmingly pondered as the most recognized and respected system for haute cuisine. (Johnson, Surlemont, Nicod, & Revaz, 2005) Page | 4 Generally, three etoile restaurants are led by highly creative and skilled chefs, emphasize on hiring high quality personnel, employ first quality ingredients and secure an exclusive wine list. Nonetheless, the absence of standardized requirements suggest an unaccountably vagueness on the rigorously selected and qualified inspectors’ accreditation criteria. (Johnson, Surlemont, Nicod, & Revaz, 2005)
Comparatively, peer reviews also face system imperfections. Anderson and Magruder (2001) encounter that there is a 49% increase on restaurant customer flow as result of a ? star increase on a Yelp rating, yet this ratings are rounded to the nearest half star which might convey an imperfect signal of quality.
Impact on Customer Behaviour Bickart and Schindler (2001) highlight the effect that online reviews originate upon customer decision-making process, as they play an influential role providing an interactive venue to share quality perception of a product or service.
Conversely, Banerjee (1922) and Bikhchandani (1988) et al. (as cited in Geraud et al. 2012) Localized conformity, fashion and heard behaviour sequence causes the purchase decision to be purely influenced by prejudice. Following preceding peers actions without contributing an own judgment leads to an information disequilibrium. (Gergaud, Storchmann, & Verardi, 2012) In accordance with Andersson and Mossberg (2004) who suggest that dining experience engrosses much more than good fooD. Gunasekeran (1992) (as cited in Upadhyay et al. 007) concurs “A restaurant takes the basic drive – the simplest act of eating – and transforms it into a civilized ritual involving hospitality, imagination, satisfaction, graciousness and warmth” (Upadhyay, Singh, & Thomas, 2007) The dining experience is sorted and evaluated in components proposed by empirical qualitative data from first round interviews (Kivela et al,1999). Primary factors empowering diners’ visit intention are the food and service quality, atmosphere, and relevant convenience factors.
Restaurant reviews focus and delineate their appraisals in these determining attributes to assist customers’ selection criteria process. (Kivela, Reece, & Inbakaran, 1999) Page | 5 Empirical evidence has also proven the assumption of the impact that social learning, thanks to technological diversification, or professional assessment evaluations indeed contain relevant information. (Luca, 2011) Subsequently, growing literature papers link the relation between restaurant revenue boost as the result of favourable reviews.
For instance, Geraud et al. 2012) finding on the comparison between the continuity on pricing level from 2004 to 2007 in NYC, considering a priori and posteriori scenarios of the inclusion of the Michelin Guide (2005) in the city, proved a substantial marginal price increase of approximately 37%. Furthermore, Luca (2011) concluded that a one – star increase in Yelp rating leads to a 5 – 9 % increase in revenue. Nonetheless, consumers’ quality perception scope through pricing signalling quality is diminishing as consumers’ knowledge widens. (Gergaud, Storchmann, & Verardi, 2012) .
Consumer Information Utilization Yet, it is unclear that the consumers’ responsiveness and utilization of the available information which is reliant on the accessibility, simplicity and trustworthiness of the actual valuable content. This hypothesis portrays the Bayesian inference which customers act upon (Luca, 2011). “Bayesian inference is a method of statistical inference that uses prior probability over some hypothesis to determine the likelihood of that hypothesis be true based on observed evidence” (Mans, 2010 , p. 1) Cai et al. 2008) conducted a randomized natural field experiment proving that assessing consumer options on menu items by providing a forged list of the top 5 selling dishes, reported an increase on demand of 13% to 20%. On the other hand, Kivela et al. (1999) explore the consumer behaviour model under the disconfirmation theory, which construes that customers compare their own dining experience with some basis gained by direct or indirect previous experiences. This might be obtained from either social or expert assessments, and the assumption that a customer will weight various restaurant attributes based on expectancy theory.
Furthermore, they studied customers’ perceptions of restaurant attributes based on demographic characteristics which shape selection criteria. (Kivela, Reece, & Inbakaran, 1999) Page | 6 Upadhyay et al. (2007) research analysis differs from the scheme that Keevela et al. (1999) suggest, since demographic variables have an insignificant impact on consumers’ preference and visit intentions. Conclusion analysis elaborates on the deciding attributes for restaurant selection, quality of food per se being the most imprescindible component.
Secondly, service quality which plays a major role in customer satisfaction or dissatisfaction and return patronage accordingly. Location, ambience and other facilities are included on the deciding factors, but disregard Keevela’s et al. (1999) finding of ambience being the fundamental factor. (Upadhyay, Singh, & Thomas, 2007)
Works Cited

Anderson, M. , & Magruder, J. (2011). Learning from the Crowd: Regression Disconinuity Estimates of the Effects of an Online Review Database. The Economic Journal ,
Berridge, K. C. (2001 ). The Phsycology of Learning .
In Reward Learning (pp. 234-242 ). Academic Press. Bouton, L. , & Kirchsteiger, G. (2011)
Good Rankings are Bad – Why Reliable Rankings Can Hurt Consumers. Centre for Economic Policy Research, 1. Cai, H. , Chen, Y. , & Fang, H. (2008).
Observational Learning: Evidence from a Randomized Natural. Yale University. Choi, J. W. , & Ok, C. (n. d. ). The Effect of Online Restaurant Reviews on Diners’ Visit Intentions. Kansas State University . Davis, M. (2009 ).
A Taste For New York; Restaurant Reviews, Food Discourse, and The Field of Gastronomy in America. New York University ,
Gergaud, O. , Storchmann, K. , & Verardi, V. (2012). Expert Opinion and Quality Perception of Consumers. Johnson, C. , Surlemont, B. , Nicod, P. , & Revaz, F. (2005).
Behind the Stars . Cornell Hotel and Restaurant Administration Quarterly , 170. Kim, S. , & Jae-Eun, C. (2010 ).
Restaurant Selection Criteria: Understading the Roles of Restaurant Type and Customers’ Sociodemographic Characteristics. Ohio State University . Kivela, J. , Reece, J. , & Inbakaran, R. (1999).
Consumer Research in the Restaurant Enviornment: Part 2 Research design and analytical methods.
International Journal of Contemporary Hospitality Management , 269 – 281. Luca, M. (2011).
Reviews, Reputation and Revenue: The Case of Yelp. com. Harvard Business School. Mans, Y. (2010 ).
Bayesian Inference. Machine Learninf Foundation , 1 . Michelin. (2011, November 29).
Retrieved from www. michelin. com Talmadge, E. (2008 , August 29). USA Today.
Retrieved from Tokyo Michelin Dispute: http://usatoday30. usatoday. com Upadhyay, Y. , Singh, S. K. , & Thomas, G. (2007). Do People Differ in

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