Information Processing on Online Review Platforms

Abstract Online reviews represent an important decision aid for consumers. Therefore, the question of whether online reviews reflect the currently available information is of high importance. Nevertheless, previous research neglects information processing on online review platforms. We address this research gap and analyze whether restaurant health inspection results have an impact on the review generation process of online restaurant reviews. We find that while severe health inspection results lead to changes of online review star ratings, information processing depends on the current environment. We find indications for corrective actions after critical health inspections: on the one hand, the restaurant health score improves, which shows an increase in restaurant quality. On the other hand, we observe indications for an increased amount of fake reviews in case of poorly-graded restaurants. We contribute to theory by providing an understanding of the nature of information processing on online review platforms. For practitioners, our findings allow for an understanding of the dynamics of online review generation. Furthermore, we outline the importance of considering the risk of deceptive behavior.

[1]  Panagiotis G. Ipeirotis,et al.  Show me the money!: deriving the pricing power of product features by mining consumer reviews , 2007, KDD '07.

[2]  Weiguo Fan,et al.  Measuring Customer Agility from Online Reviews Using Big Data Text Analytics , 2018, J. Manag. Inf. Syst..

[3]  Raj Sharman,et al.  Impact of User-Generated Internet Content on Hospital Reputational Dynamics , 2018, J. Manag. Inf. Syst..

[4]  Lihua Huang,et al.  Promotional Marketing or Word-of-Mouth? Evidence from Online Restaurant Reviews , 2013, Inf. Syst. Res..

[5]  Han Zhang,et al.  Anxious or Angry? Effects of Discrete Emotions on the Perceived Helpfulness of Online Reviews , 2014, MIS Q..

[6]  Lorin M. Hitt,et al.  Self Selection and Information Role of Online Product Reviews , 2007, Inf. Syst. Res..

[7]  Georgios Zervas,et al.  Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud , 2015, Manag. Sci..

[8]  SchuffDavid,et al.  What makes a helpful online review? a study of customer reviews on amazon.com , 2010 .

[9]  S. Henson,et al.  CONSUMER ASSESSMENT OF THE SAFETY OF RESTAURANTS: THE ROLE OF INSPECTION NOTICES AND OTHER INFORMATION CUES , 2006 .

[10]  Wei Chen,et al.  The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings , 2011, Comput. Hum. Behav..

[11]  G. Jin,et al.  The Effect of Information on Product Quality: Evidence from Restaurant Hygiene Grade Cards , 2002 .

[12]  George Valkanas,et al.  The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry , 2016, Inf. Syst. Res..

[13]  Efraim Turban,et al.  Introduction to the Special Issue Social Commerce: A Research Framework for Social Commerce , 2011, Int. J. Electron. Commer..

[14]  Yejin Choi,et al.  Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online Reviews , 2013, EMNLP.

[15]  Jan Muntermann,et al.  Explaining and predicting online review helpfulness: The role of content and reviewer-related signals , 2018, Decis. Support Syst..

[16]  Kenneth R. Ahern,et al.  Sample Selection and Event Study Estimation , 2008 .

[17]  Jianqing Chen,et al.  Product Reviews : Implications for Retailers and Competing Manufacturers , 2013 .

[18]  X. Zhang,et al.  Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics , 2010 .

[19]  Ching-man Au Yeung,et al.  "Popularity Effect" in User-Generated Content: Evidence from Online Product Reviews , 2014, Inf. Syst. Res..

[20]  A. Mackinlay,et al.  Event Studies in Economics and Finance , 1997 .

[21]  David Schuff,et al.  What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com , 2010 .

[22]  A. Lo.  The Adaptive Markets Hypothesis , 2004 .

[23]  Soo-Min Kim,et al.  Automatically Assessing Review Helpfulness , 2006, EMNLP.

[24]  Daniel E. O'Leary,et al.  Event Study Methodologies in Information Systems Research , 2011, Int. J. Account. Inf. Syst..

[25]  Sebastian Raschka,et al.  Python Machine Learning , 2015 .

[26]  Michael Luca,et al.  Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud , 2015 .

[27]  Christie M. Fuller,et al.  An Examination and Validation of Linguistic Constructs for Studying High-Stakes Deception , 2013 .

[28]  Luis Gravano,et al.  Using Online Reviews by Restaurant Patrons to Identify Unreported Cases of Foodborne Illness — New York City, 2012–2013 , 2014, MMWR. Morbidity and mortality weekly report.

[29]  Chong Wang,et al.  Socially Nudged: A Quasi-Experimental Study of Friends' Social Influence in Online Product Ratings , 2018, Inf. Syst. Res..

[30]  E. Fama,et al.  The Adjustment of Stock Prices to New Information , 1969 .

[31]  Jae H. Kim,et al.  Exchange-Rate Return Predictability and the Adaptive Markets Hypothesis: Evidence from Major Foreign Exchange Rates , 2012 .

[32]  Iryna Pentina,et al.  Exploring effects of source similarity, message valence, and receiver regulatory focus on yelp review persuasiveness and purchase intentions , 2018 .

[33]  B. Sparks,et al.  The impact of online reviews on hotel booking intentions and perception of trust. , 2011 .

[34]  J. Wu,et al.  Disconfirmation Effect on Online Rating Behavior: A Structural Model , 2014, Inf. Syst. Res..

[35]  Nitin Aggarwal,et al.  The More, the Merrier? How the Number of Partners in a Standard-Setting Initiative Affects Shareholder's Risk and Return , 2011, MIS Q..

[36]  Christopher J. Neely,et al.  The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market , 2009, Journal of Financial and Quantitative Analysis.

[37]  Boi Faltings,et al.  Understanding user behavior in online feedback reporting , 2007, EC '07.

[38]  J. Nunamaker,et al.  Automating Linguistics-Based Cues for Detecting Deception in Text-Based Asynchronous Computer-Mediated Communications , 2004 .

[39]  Claire Cardie,et al.  Towards a General Rule for Identifying Deceptive Opinion Spam , 2014, ACL.

[40]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[41]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[42]  Sung-Byung Yang,et al.  Research Note - Using Real Options to Investigate the Market Value of Virtual World Businesses , 2012, Inf. Syst. Res..

[43]  Matthew L. Jensen,et al.  Credibility of Anonymous Online Product Reviews: A Language Expectancy Perspective , 2013, J. Manag. Inf. Syst..

[44]  Prem C. Jain,et al.  The behavior of daily stock market trading volume , 1989 .

[45]  Chrysanthos Dellarocas,et al.  Are Consumers More Likely to Contribute Online Reviews for Hit or Niche Products? , 2010, J. Manag. Inf. Syst..

[46]  Steven A. Mccornack.  Information manipulation theory , 1992 .

[47]  Varun Grover,et al.  Research Report: A Reexamination of IT Investment and the Market Value of the Firm - An Event Study Methodology , 2001, Inf. Syst. Res..

[48]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[49]  B. Sparks,et al.  Online travel reviews as persuasive communication: The effects of content type, source, and certification logos on consumer behavior , 2013 .

[50]  Peter Gomber,et al.  How to enable automated trading engines to cope with news-related liquidity shocks? Extracting signals from unstructured data , 2014, Decis. Support Syst..

[51]  Wonseok Oh,et al.  Value Cocreation and Wealth Spillover in Open Innovation Alliances , 2012, MIS Q..

[52]  Yao Liu,et al.  Using contextual features and multi-view ensemble learning in product defect identification from online discussion forums , 2018, Decis. Support Syst..

[53]  Eric K. Clemons,et al.  When Online Reviews Meet Hyperdifferentiation: A Study of Craft Beer Industry , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[54]  Mike Chiasson,et al.  The Ends of Information Systems Research: A Pragmatic Framework , 2012, MIS Q..

[55]  D. Worsfold,et al.  Consumer information on hygiene inspections of food premises , 2006 .

[56]  Jiang Wu,et al.  Online Customer Reviews and Consumer Evaluation: The Role of Review Font , 2017, Inf. Manag..

[57]  Amit V. Deokar,et al.  Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews , 2018, Decis. Support Syst..

[58]  Hailiang Chen,et al.  Monetary Incentive and Stock Opinions on Social Media , 2019, J. Manag. Inf. Syst..

[59]  Xiao Ma,et al.  Impact of Prior Reviews on the Subsequent Review Process in Reputation Systems , 2013, J. Manag. Inf. Syst..

[60]  Ritu Agarwal,et al.  Vocal Minority and Silent Majority: How Do Online Ratings Reflect Population Perceptions of Quality? , 2015, MIS Q..

[61]  Zhiqiang Zheng,et al.  Are New IT-Enabled Investment Opportunities Diminishing for Firms? , 2012, Inf. Syst. Res..

[62]  Henry A. Kautz,et al.  nEmesis: Which Restaurants Should You Avoid Today? , 2013, HCOMP.

[63]  Michelle R. Worosz,et al.  Dining for Safety: Consumer Perceptions of Food Safety and Eating Out , 2009 .

[64]  Anindya Ghose,et al.  Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets , 2008, Inf. Syst. Res..

[65]  Chrysanthos Dellarocas,et al.  The Impact of Online Product Reviews on Product Returns , 2018, Inf. Syst. Res..

[66]  Srinivasan Rangan.  Earnings management and the performance of seasoned equity offerings , 1998 .

[67]  Oded Nov,et al.  The Persuasive Power of Algorithmic and Crowdsourced Advice , 2018, J. Manag. Inf. Syst..

[68]  Weiguo Fan,et al.  Combining Geographical and Social Influences with Deep Learning for Personalized Point-of-Interest Recommendation , 2018, J. Manag. Inf. Syst..

[69]  Michael Scholz,et al.  Think Twice Before You Buy! How Recommendations Affect Three-Stage Purchase Decision Processes , 2013, ICIS.

[70]  Douglas Powell,et al.  The use of restaurant inspection disclosure systems as a means of communicating food safety information , 2009 .

[71]  Yiming Yang,et al.  RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..