The authors examine the effects of a firm’s and its competitors’ online reviews on its demand within the hotel industry. The authors leverage a unique data set of actual bookings from properties of a major hotel chain in six different markets in the United States, supplemented with online reviews garnered from a popular social media platform. The findings indicate that not only a hotel’s own reviews but also its competitors’ reviews have a significant impact on the hotel’s booking performance. The impact of review sentiment is amplified if the focal hotel also charges higher prices or when the volume of reviews is high. The authors establish heterogeneous effects across consumer segments (business vs. leisure travelers) and by the type of review content (objective vs. subjective attributes to assess quality). Specifically, both a hotel’s own reviews and its competitors’ reviews have a larger impact on bookings for business travelers compared with leisure travelers, and for reviews that mainly discuss subjective attributes, for which consumers need to rely on the experiences of others to assess the quality of a hotel prior to their stay. The study provides a set of comprehensive insights on the impact of both own and competitors’ online reviews on a focal hotel’s bookings.
AdhikariAtanu (2015), “Differentiating Subjective and Objective Attributes of Experience Products to Estimate Willingness to Pay Price Premium,” Journal of Travel Research, 54 (5), 634–44.
2.
AhluwaliaRohiniUnnavaH. RaoBurnkrantRobert E. (2001), “The Moderating Role of Commitment on the Spillover Effect of Marketing Communications,” Journal of Marketing Research, 38 (4), 458–70.
3.
AndersonChris K. (2012), “The Impact of Social Media on Lodging Performance,” Cornell Hospitality Report, 12 (15), 4–11.
4.
ArchakNikolayGhoseAnindyaIpeirotisPanagiotis G. (2011), “Deriving the Pricing Power of Product Features by Mining Consumer Reviews,” Management Science, 57 (8), 1485–1509.
5.
ArellanoManuelBondStephen (1991), “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Review of Economic Studies, 58 (2), 277–97.
6.
AscarzaEvaNetzerOdedHardieBruce G.S. (2018), “Some Customers Would Rather Leave Without Saying Goodbye,” Marketing Science, 37 (1), 54–77.
7.
Babić RosarioAnaSotgiuFrancescaDe ValckKristineBijmoltTammo H.A. (2016), “The Effect of Electronic Word of Mouth on Sales: A Meta-Analytic Review of Platform, Product, and Metric Factors,” Journal of Marketing Research, 53 (3), 297–318.
8.
BanerjeeShrabasteeDellarocasChrisZervasGeorgios (2021), “Interacting User-Generated Content Technologies: How Questions and Answers Affect Consumer Reviews,” Journal of Marketing Research, 58 (4), 742–61.
9.
BleiDavid M.NgAndrew Y.JordanMichael I. (2003), “Latent Dirichlet Allocation,” Journal of Machine Learning Research, 3, 993–1022.
10.
BorahAbhishekTellisGerard J. (2016), “Halo (Spillover) Effects in Social Media: Do Product Recalls of One Brand Hurt or Help Rival Brands?” Journal of Marketing Research, 53 (2), 143–60.
ChenYuboLiuYongZhangJurui (2012), “When Do Third-Party Product Reviews Affect Firm Value and What Can Firms Do? The Case of Media Critics and Professional Movie Reviews,” Journal of Marketing, 76 (2), 116–34.
15.
ChevalierJudith A.MayzlinDina (2006), “The Effect of Word of Mouth on Sales: Online Book Reviews,” Journal of Marketing Research, 43 (3), 345–54.
16.
ChintaguntaPradeep K.GopinathShyamVenkataramanSriram (2010), “The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets,” Marketing Science, 29 (5), 944–57.
17.
CraggJohn G.DonaldStephen G. (1993), “Testing Identifiability and Specification in Instrumental Variable Models,” Econometric Theory, 9 (2), 222–40.
18.
DanaherPeter J.BonfrerAndréDharSanjay (2008), “The Effect of Competitive Advertising Interference on Sales for Packaged Goods,” Journal of Marketing Research, 45 (2), 211–25.
19.
De los SantosBaburKoulayevSergei (2017), “Optimizing Click-Through in Online Rankings with Endogenous Search Refinement,” Marketing Science, 36 (4), 542–64.
20.
DharVasantChangElaine A. (2009), “Does Chatter Matter? The Impact of User-Generated Content on Music Sales,” Journal of Interactive Marketing, 23 (4), 300–307.
21.
DuanWenjingGuBinWhinstonAndrew B. (2008), “The Dynamics of Online Word-of-Mouth and Product Sales—An Empirical Investigation of the Movie Industry,” Journal of Retailing, 84 (2), 233–42.
22.
ErdemTülinSunBaohong (2002), “An Empirical Investigation of the Spillover Effects of Advertising and Sales Promotions in Umbrella Branding,” Journal of Marketing Research, 39 (4), 408–20.
23.
FeldmanJack M.LynchJohn G. (1988), “Self-Generated Validity and Other Effects of Measurement on Belief, Attitude, Intention, and Behavior,” Journal of Applied Psychology, 73 (3), 421.
24.
FilieriRaffaele (2015), “What Makes Online Reviews Helpful? A Diagnosticity-Adoption Framework to Explain Informational and Normative Influences in E-WOM,” Journal of Business Research, 68 (6), 1261–70.
25.
GhoseAnindyaIpeirotisPanagiotis G.LiBeibei (2014), “Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue,” Management Science, 60 (7), 1632–54.
26.
HuNanPavlouPaul A.ZhangJie J. (2017), “On Self-Selection Biases in Online Product Reviews,” MIS Quarterly, 41 (2), 449–71.
27.
HuiSam K.InmanJ. JeffreyHuangYanliuSuherJacob (2013), “The Effect of In-Store Travel Distance on Unplanned Spending: Applications to Mobile Promotion Strategies,” Journal of Marketing, 77 (2), 1–16.
28.
JanakiramanRamkumarSismeiroCatarinaDuttaShantanu (2009), “Competitive Spillovers Across Competing Brands: A Disaggregate Model of How and When,” Journal of Marketing Research, 46 (4), 467–81.
29.
KaramanHülya (2021), “Online Review Solicitations Reduce Extremity Bias in Online Review Distributions and Increase Their Representativeness,” Management Science, 67 (7), 4420–45.
30.
KimWoo GonLimHyunjungBrymerRobert A. (2015), “The Effectiveness of Managing Social Media on Hotel Performance,” International Journal of Hospitality Management, 44, 165–71.
31.
KleibergenFrankPaapRichard (2006), “Generalized Reduced Rank Tests Using the Singular Value Decomposition,” Journal of Econometrics, 133 (1), 97–126.
32.
KoulayevSergei (2014), “Search for Differentiated Products: Identification and Estimation,” RAND Journal of Economics, 45 (3), 553–75.
33.
KumarAshishBezawadaRamRishikaRishikaJanakiramanRamkumarKannanP.K. (2016), “From Social to Sale: The Effects of Firm-Generated Content in Social Media on Customer Behavior,” Journal of Marketing, 80 (1), 7–25.
34.
LeeYoung-JinHosanagarKartikTanYong (2015), “Do I Follow My Friends or the Crowd? Information Cascades in Online Movie Ratings,” Management Science, 61 (9), 2241–58.
35.
LewisGregoryZervasGeorgios (2016), “The Welfare Impact of Consumer Reviews: A Case Study of the Hotel Industry,” working paper, Questrom School of Business, Boston University.
36.
LoughranTimMcDonaldBill (2011), “When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks,” Journal of Finance, 66 (1), 35–65.
37.
LucaMichael (2016), “Reviews, Reputation, and Revenue: The Case of Yelp.com,” Working Paper 12-016, Harvard Business School.
38.
LudwigStephanDe RuyterKoFriedmanMikeBrüggenElisabeth C.WetzelsMartinPfannGerard (2013), “More Than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates,” Journal of Marketing, 77 (1), 87–103.
39.
MankadShawnHanHyunjeong SpringGohJoelGavirneniSrinagesh (2016), “Understanding Online Hotel Reviews Through Automated Text Analysis,” Service Science, 8 (2), 124–38.
40.
McAfeeAndrewBrynjolfssonErik (2012), “Big Data: The Management Revolution,” Harvard Business Review, 90 (10), 60–68.
41.
MudambiSusan M.SchuffDavid (2010), “What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com,” MIS Quarterly, 34 (1), 185–200.
42.
NeweyWhitney K.WestKenneth D. (1987), “Hypothesis Testing with Efficient Method of Moments Estimation,” International Economic Review, 28 (3), 777–87.
43.
NooneBreffni M.McGuireKelly A. (2013), “Pricing in a Social World: The Influence of Non-Price Information on Hotel Choice,” Journal of Revenue and Pricing Management, 12 (5), 385–401.
44.
PangBoLeeLillian (2008), “Opinion Mining and Sentiment Analysis,” Foundations and Trends® in Information Retrieval, 2 (1–2), 1–135.
45.
PapiesDominikEbbesPetervan HeerdeHarald J. (2017), “Addressing Endogeneity in Marketing Models,” in Advanced Methods for Modeling Markets, LeeflangPeter S.H.WieringaJaap E.BijmoltTammo H.A.PauwelsKoen H., eds. Springer, 581–627.
46.
PekgünPelinMenichRonald P.AcharyaSureshFinchPhillip G.DeschampsFredericMalleryKathleenVan SistineJimChristiansonKyleFullerJames (2013), “Carlson Rezidor Hotel Group Maximizes Revenue Through Improved Demand Management and Price Optimization,” INFORMS Journal on Applied Analytics (Interfaces), 43 (1), 21–36.
47.
RehurekRadimSojkaPetr (2010), “Software Framework for Topic Modelling with Large Corpora,” Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, https://radimrehurek.com/lrec2010_final.pdf.
48.
RishikaRishikaKumarAshishJanakiramanRamkumarBezawadaRam (2013), “The Effect of Customers’ Social Media Participation on Customer Visit Frequency and Profitability: An Empirical Investigation,” Information Systems Research, 24 (1), 108–27.
49.
RöderMichaelBothAndreasHinneburgAlexander (2015), “Exploring the Space of Topic Coherence Measures,” in Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, 399–408.
50.
RoehmMichelle L.TyboutAlice M. (2006), “When Will a Brand Scandal Spill Over, and How Should Competitors Respond?” Journal of Marketing Research, 43 (3), 366–73.
51.
RossiPeter E. (2014), “Even the Rich Can Make Themselves Poor: A Critical Examination of IV Methods in Marketing Applications,” Marketing Science, 33 (5), 655–72.
StaigerDouglasStockJames H. (1994), “Instrumental Variables Regression with Weak Instruments,” Econometrica: Journal of the Econometric Society, 65 (3), 557–86.
TirunillaiSeshadriTellisGerard J. (2012), “Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance,” Marketing Science, 31 (2), 198–215.
59.
TirunillaiSeshadriTellisGerard J. (2014), “Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation,” Journal of Marketing Research, 51 (4), 463–79.
VermeulenIvar E.SeegersDaphne (2009), “Tried and Tested: The Impact of Online Hotel Reviews on Consumer Consideration,” Tourism Management, 30 (1), 123–7.
64.
WatsonJaredGhoshAnastasiya PocheptsovaTrusovMichael (2018), “Swayed by the Numbers: The Consequences of Displaying Product Review Attributes,” Journal of Marketing, 82 (6), 109–31.
ZervasGeorgiosProserpioDavideByersJohn W. (2017), “The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry,” Journal of Marketing Research, 54 (5), 687–705.
67.
ZhuFengZhangXiaoquan (2010), “Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics,” Journal of Marketing, 74 (2), 133–48.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
