The authors investigate how the geographical distance between online users is associated with electronic word-of-mouth (eWOM) effectiveness. Their research leverages variation in the visibility of eWOM messages on the social media platform of Twitter to address the issue of correlated user behaviors and preferences. The study shows that the likelihood that followers who are exposed to users’ WOM subsequently make purchases increases with followers’ geographic proximity to the users. The authors propose social identification as a potential mechanism for why geographical distance still matters online in eWOM: because consumers may form a sense of social identity based on their physical location, information regarding the spatial proximity of users could trigger online social identification with others. The findings are robust to alternative methods and specifications, such as further controlling for latent user homophily by incorporating user characteristics and embeddings based on advanced machine-learning and deep-learning models and a corpus of 140 million messages. The authors also rule out several alternative explanations. The findings have important implications for platform design, content curation, and seeding and targeting strategies.
AdamopoulosPanagiotisGhoseAnindyaTodriVilma (2018), “The Impact of User Personality Traits on Word-of-Mouth: Text-Mining Social Media Platforms,” Information Systems Research, 29 (3), 612–40.
AdamopoulosPanagiotisTodriVilma (2014), “Social Media Analytics: The Effectiveness of Promotional Events on Brand User Base in Social Media,” in Proceedings of the 35th International Conference on Information Systems (ICIS ’14). Auckland, New Zealand: AIS, https://aisel.aisnet.org/icis2014/proceedings/SocialMedia/8/.
4.
AdamopoulosPanagiotisTodriVilma (2015a), “The Effectiveness of Marketing Strategies in Social Media: Evidence from Promotional Events,” in Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’15). New York: Association for Computing Machinery, 1641–50.
AdamopoulosPanagiotisTodriVilmaGhoseAnindya (2020), “Demand Effects of the Internet-of-Things Sales Channel: Evidence from Automating the Purchase Process,” Information Systems Research, 32 (1), 238–67.
7.
AgrawalAjayCataliniChristianGoldfarbAvi (2015), “Crowdfunding: Geography, Social Networks, and the Timing of Investment Decisions,” Journal of Economics & Management Strategy, 24 (2), 253–74.
8.
AgrawalAjayGoldfarbAvi (2008), “Restructuring Research: Communication Costs and the Democratization of University Innovation,” American Economic Review, 98 (4), 1578–90.
9.
AmeriMinaHonkaElisabethXieYing (2019), “Word of Mouth, Observed Adoptions, and Anime-Watching Decisions: The Role of the Personal vs. the Community Network,” Marketing Science, 38 (4), 567–83.
AralSinanMuchnikLevSundararajanArun (2009), “Distinguishing Influence-Based Contagion from Homophily-Driven Diffusion in Dynamic Networks,” Proceedings of the National Academy of Sciences of the United States of America, 106 (51), 21544–49.
12.
AralSinanNicolaidesChristos (2017), “Exercise Contagion in a Global Social Network,” Nature Communications, 8, 14753.
13.
AralSinanWalkerDylan (2012), “Identifying Influential and Susceptible Members of Social Networks,” Science, 337 (6092), 337–41.
14.
AralSinanWalkerDylan (2014), “Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment,” Management Science, 60 (6), 1352–70.
15.
ArchakNikolayGhoseAnindyaIpeirotisPanagiotis G. (2011), “Deriving the Pricing Power of Product Features by Mining Consumer Reviews,” Management Science, 57 (8), 1485–1509.
16.
ArunR.SureshVenkatasubramaniyanVeni MadhavanC.E.Narasimha MurthyM.N. (2010), “On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations,” in Advances in Knowledge Discovery and Data Mining, Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, and Vikram Pudi, eds. Springer, 391–402.
17.
AshmoreRichard D.DeauxKayMcLaughlin-VolpeTracy (2004), “An Organizing Framework for Collective Identity: Articulation and Significance of Multidimensionality,” Psychological Bulletin, 130 (1), 80–114.
18.
AtefiYasharAhearneMichaelMaxhamJames G.IIIDonavanD. ToddCarlsonBrad D. (2018), “Does Selective Sales Force Training Work?” Journal of Marketing Research, 55 (5), 722–37.
19.
AustinPeter C. (2011), “An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies,” Multivariate Behavioral Research, 46 (3), 399–424.
BakerAndrew M.DonthuNaveenKumarV. (2016), “Investigating How Word-of-Mouth Conversations About Brands Influence Purchase and Retransmission Intentions,” Journal of Marketing Research, 53 (2), 225–39.
22.
BellDavid R.SongSangyoung (2007), “Neighborhood Effects and Trial on the Internet: Evidence from Online Grocery Retailing,” Quantitative Marketing and Economics, 5 (4), 361–400.
23.
BergerJonahSchwartzEric M. (2011), “What Drives Immediate and Ongoing Word of Mouth?” Journal of Marketing Research, 48 (5), 869–80.
24.
BhattacharyaParantapaZafarMuhammad BilalGangulyNiloyGhoshSaptarshiGummadiKrishna P. (2014), “Inferring User Interests in the Twitter Social Network,” in Proceedings of the 8th ACM Conference on Recommender Systems (RecSys '14), https://people.mpi-sws.org/∼gummadi/papers/recsys14-userinterests.pdf.
25.
BleiDavid M.NgAndrew Y.JordanMichael I. (2003), “Latent Dirichlet Allocation,” Journal of Machine Learning Research, 3 (4–5), 993–1022.
BlumBernardo S.GoldfarbAvi (2006), “Does the Internet Defy the Law of Gravity?” Journal of International Economics, 70 (2), 384–405.
28.
BrookhartM. AlanSchneeweissSebastianRothmanKenneth J.GlynnRobert J.AvornJerryStürmerTil (2006), “Variable Selection for Propensity Score Models,” American Journal of Epidemiology, 163 (12), 1149–56.
29.
CairncrossFrances (1997), The Death of Distance: How the Communications Revolution Will Change Our Lives. Cambridge, MA: Harvard Business School.
30.
CairncrossFrances (2001), The Death of Distance 2.0. London: Texere.
31.
ChaikenShelly (1987), “The Heuristic Model of Persuasion,” in Social Influence: The Ontario Symposium, Vol. 5. Hillsdale, NJ: Lawrence Erlbaum Associates.
32.
ChaikenShellyMaheswaranDurairaj (1994), “Heuristic Processing Can Bias Systematic Processing: Effects of Source Credibility, Argument Ambiguity, and Task Importance on Attitude Judgment,” Journal of Personality and Social Psychology, 66 (3), 460–73.
33.
ChenXiVan Der LansRalfPhanTuan Q. (2017), “Uncovering the Importance of Relationship Characteristics in Social Networks: Implications for Seeding Strategies,” Journal of Marketing Research, 54 (2), 187–201.
34.
ChevalierJudith A.MayzlinDina (2006), “The Effect of Word of Mouth on Sales: Online Book Reviews,” Journal of Marketing Research, 43 (3), 345–54.
35.
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.
36.
ChoiJeonghyeBellDavid R. (2011), “Preference Minorities and the Internet,” Journal of Marketing Research, 48 (4), 670–82.
37.
ChoiJeonghyeHuiSam K.BellDavid R. (2010), “Spatiotemporal Analysis of Imitation Behavior Across New Buyers at an Online Grocery Retailer,” Journal of Marketing Research, 47 (1), 75–89.
38.
CialdiniRobert (2016), Pre-suasion: A Revolutionary Way to Influence and Persuade. New York: Simon and Schuster.
39.
ColemanPeter T.DeutschMortonMarcusEric C. (2014), The Handbook of Conflict Resolution: Theory and Practice. Hoboken, NJ: John Wiley & Sons.
40.
CoxD.R. (1972), “Regression Models and Life-Tables,” Journal of the Royal Statistical Society. Series B (Methodological), 34 (2), 187–220.
41.
CulottaAronCutlerJennifer (2016), “Mining Brand Perceptions from Twitter Social Networks,” Marketing Science, 35 (3), 343–62.
42.
De VeirmanMarijkeCaubergheVerolineHuddersLiselot (2017), “Marketing Through Instagram Influencers: The Impact of Number of Followers and Product Divergence on Brand Attitude,” International Journal of Advertising, 36 (5), 798–828.
FormanChrisGhoseAnindyaGoldfarbAvi (2009), “Competition Between Local and Electronic Markets: How the Benefit of Buying Online Depends on Where You Live,” Management Science, 55 (1), 47–57.
45.
FormanChrisGhoseAnindyaWiesenfeldBatia (2008), “Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets,” Information Systems Research, 19 (3), 291–313.
46.
FossenBeth L.AndrewsMichelleSchweidelDavid A. (2017), “Sociodemographic Versus Geographic Proximity in the Diffusion of Online Conversations,” Journal of the Association for Consumer Research, 2 (2), 246–66.
47.
GhoseAnindyaSinghParam VirTodriVilma (2017), “Got Annoyed? Examining the Advertising Effectiveness and Annoyance Dynamics,” in Proceedings of the 38th International Conference on Information Systems (ICIS ’17). Seoul, Korea: AIS, https://aisel.aisnet.org/icis2017/DataScience/Presentations/21/.
48.
GhoseAnindyaTodri-AdamopoulosVilma (2016), “Towards a Digital Attribution Model: Measuring the Impact of Display Advertising on Online Consumer Behavior,” MIS Quarterly, 40 (4), 889–910.
49.
GodesDavidMayzlinDina (2004), “Using Online Conversations to Study Word-of-Mouth Communication,” Marketing Science, 23 (4), 545–60.
50.
GodesDavidMayzlinDina (2009), “Firm-Created Word-of-Mouth Communication: Evidence from a Field Test,” Marketing Science, 28 (4), 721–39.
GrahamStephen (1998), “The End of Geography or the Explosion of Place? Conceptualizing Space, Place and Information Technology,” Progress in Human Geography, 22 (2), 165–85.
54.
GroverAdityaLeskovecJure (2016), “node2vec: Scalable Feature Learning for Networks,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). New York: Association for Computing Machinery, 855–64.
55.
HamptonKeithWellmanBarry (2003), “Neighboring in Netville: How the Internet Supports Community and Social Capital in a Wired Suburb,” City & Community, 2 (4), 277–311.
56.
HeereBobWalkerMatthewYoshidaMasayukiKoYong JaeJordanJeremy S.JamesJeffrey D. (2011), “Brand Community Development Through Associated Communities: Grounding Community Measurement Within Social Identity Theory,” Journal of Marketing Theory and Practice, 19 (4), 407–22.
57.
HindsPamela J.MortensenMark (2005), “Understanding Conflict in Geographically Distributed Teams: The Moderating Effects of Shared Identity, Shared Context, and Spontaneous Communication,” Organization Science, 16 (3), 290–307.
58.
HinzOliverSkieraBerndBarrotChristianBeckerJan U. (2011), “Seeding Strategies for Viral Marketing: An Empirical Comparison,” Journal of Marketing, 75 (6), 55–71.
HortaçsuAliMartínez-JerezF. AsísDouglasJason (2009), “The Geography of Trade in Online Transactions: Evidence from eBay and Mercadolibre,” American Economic Journal: Microeconomics, 1 (1), 53–74.
61.
ImbensGuido W.RubinDonald B. (2015), Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge University Press.
62.
ItoJunSongJingTodaHiroyukiKoikeYoshimasaOyamaSatoshi (2015), “Assessment of Tweet Credibility with LDA Features,” in Proceedings of the 24th International Conference on World Wide Web. New York: Association for Computing Machinery, 953–58.
63.
JacobsNicholas F.MunisB. Kal (2020), “Staying in Place: Federalism and the Political Economy of Place Attachment,” Publius: The Journal of Federalism, 50 (4), 544–65.
64.
JenkinsRichard (2014), Social Identity, 4th ed. London: Routledge.
65.
KalbfleischJohn D.PrenticeRoss L. (2011), The Statistical Analysis of Failure Time Data. Hoboken, NJ: John Wiley & Sons.
66.
KatonaZsoltZubcsekPeter PalSarvaryMiklos (2011), “Network Effects and Personal Influences: The Diffusion of an Online Social Network,” Journal of Marketing Research, 48 (3), 425–43.
67.
KullbackSolomonLeiblerRichard A. (1951), “On Information and Sufficiency,” Annals of Mathematical Statistics, 22 (1), 79–86.
68.
LiXinxinHittLorin M. (2008), “Self-Selection and Information Role of Online Product Reviews,” Information Systems Research, 19 (4), 456–74.
69.
LiChenxiLuoXuemingZhangChengWangXiaoyi (2017), “Sunny, Rainy, and Cloudy with a Chance of Mobile Promotion Effectiveness,” Marketing Science, 36 (5), 762–79.
70.
LinMingfengViswanathanSiva (2015), “Home Bias in Online Investments: An Empirical Study of an Online Crowdfunding Market,” Management Science, 62 (5), 1393–1414.
71.
LiuYong (2006), “Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue,” Journal of Marketing, 70 (3), 74–89.
72.
LovettMitchell J.PeresRenanaShacharRon (2013), “On Brands and Word of Mouth,” Journal of Marketing Research, 50 (4), 427–44.
73.
LovettMitchell J.StaelinRichard (2016), “The Role of Paid, Earned, and Owned Media in Building Entertainment Brands: Reminding, Informing, and Enhancing Enjoyment,” Marketing Science, 35 (1), 142–57.
74.
MaLiyeKrishnanRamayyaMontgomeryAlan L. (2014), “Latent Homophily or Social Influence? An Empirical Analysis of Purchase Within a Social Network,” Management Science, 61 (2), 454–73.
75.
ManningChristopherRaghavanPrabhakarSchützeHinrich (2008), Introduction to Information Retrieval. Cambridge, UK: Cambridge University Press.
76.
ManskiCharles F. (1993), “Identification of Endogenous Social Effects: The Reflection Problem,” Review of Economic Studies, 60 (3), 531–42.
77.
ManskiCharles F. (1999), Identification Problems in the Social Sciences. Cambridge, MA: Harvard University Press.
78.
McGartyCraigHaslamS. AlexanderHutchinsonKaren J.TurnerJohn C. (1994), “The Effects of Salient Group Memberships on Persuasion,” Small Group Research, 25 (2), 267–93.
79.
McPhersonMillerSmith-LovinLynnCookJames M. (2001), “Birds of a Feather: Homophily in Social Networks,” Annual Review of Sociology, 27 (1), 415–44.
80.
MeynersJannikBarrotChristianBeckerJan U.GoldenbergJacob (2017), “The Role of Mere Closeness: How Geographic Proximity Affects Social Influence,” Journal of Marketing, 81 (5), 49–66.
81.
MomtaziSaeedehNaumannFelix (2013), “Topic Modeling for Expert Finding Using Latent Dirichlet Allocation,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3 (5), 346–53.
NaylorRebecca WalkerLambertonCait PoynorNortonDavid A. (2011), “Seeing Ourselves in Others: Reviewer Ambiguity, Egocentric Anchoring, and Persuasion,” Journal of Marketing Research, 48 (3), 617–31.
84.
NewmanO. (1972), Defensible Space: Crime Prevention Through Urban Design. New York: Macmillan.
85.
OwoputiOlutobiO’ConnorBrendanDyerChrisGimpelKevinSchneiderNathanSmithNoah A. (2013), “Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters,” in HLT-NAACL, https://aclanthology.org/N13-1039.pdf.
86.
PackardGrantBergerJonah (2017), “How Language Shapes Word of Mouth's Impact,” Journal of Marketing Research, 54 (4), 572–88.
87.
PackardGrantGershoffAndrew D.WootenDavid B. (2016), “When Boastful Word of Mouth Helps Versus Hurts Social Perceptions and Persuasion,” Journal of Consumer Research, 43 (1), 26–43.
88.
ParkEunhoRishikaRishikaJanakiramanRamkumarHoustonMark B.YooByungjoon (2018), “Social Dollars in Online Communities: The Effect of Product, User, and Network Characteristics,” Journal of Marketing, 82 (1), 93–114.
89.
PerozziBryanAl-RfouRamiSkienaSteven (2014), “Deepwalk: Online Learning of Social Representations,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '14). New York: Association for Computing Machinery.
90.
ProvostFosterFawcettTom (2013), Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. Sebastopol, CA: O’Reilly Media.
SmithJoanne R.HoggMichael A. (2008), “Social Identity and Attitudes,” in Attitudes and Attitude Change, William D. Crano and Radmila Prislin, eds. New York: Psychology Press, 337–60.
98.
SonJaebongLeeJintaeLarsenKai R.WooJiyoung (2019), “Understanding the Uncertainty of Disaster Tweets and Its Effect on Retweeting: The Perspectives of Uncertainty Reduction Theory and Information Entropy,” Journal of the Association for Information Science and Technology, 71 (10), 1145–61.
99.
StephenAndrew T.LehmannDonald R. (2016), “How Word-of-Mouth Transmission Encouragement Affects Consumers’ Transmission Decisions, Receiver Selection, and Diffusion Speed,” International Journal of Research in Marketing, 33 (4), 755–66.
100.
SunChenshuoAdamopoulosPanagiotisGhoseAnindyaLuoXueming (2021), “Predicting Stages in the Consumer Path-Purchase Journey: An Omnichannel Deep-Learning Model,” Information Systems Research, https://ssrn.com/abstract=3814630 or http://dx.doi.org/10.2139/ssrn.3814630.
101.
TajfelHenri (1974), “Social Identity and Intergroup Behaviour,” Information (International Social Science Council), 13 (2), 65–93.
102.
TaylorRalph B.GottfredsonStephen D.BrowerSidney (1985), “Attachment to Place: Discriminant Validity, and Impacts of Disorder and Diversity,” American Journal of Community Psychology, 13 (5), 525–42.
103.
TodriVilma (2021), “Frontiers: The Impact of Ad-Blockers on Online Consumer Behavior,” Marketing Science, https://doi.org/10.1287/mksc.2021.1309.
104.
TodriVilmaAdamopoulosPanagiotis (2014), “Social Commerce: An Empirical Examination of the Antecedents and Consequences of Commerce in Social Network Platforms,” in Proceedings of the 35th International Conference on Information Systems (ICIS ’14). Auckland, New Zealand: AIS, https://aisel.aisnet.org/icis2014/proceedings/EBusiness/54/.
105.
TodriVilmaGhoseAnindyaSinghParam Vir (2020), “Trade-Offs in Online Advertising: Advertising Effectiveness and Annoyance Dynamics Across the Purchase Funnel,” Information Systems Research, 31 (1), 102–25.
106.
TrainKenneth E. (2003), Discrete Choice Methods with Simulation. Cambridge, UK: Cambridge University Press.
107.
TurnerJohn C. (1982), “Towards a Cognitive Redefinition of the Social Group,” in Social Identity and Intergroup Relations, Henri Tajfel, ed. Cambridge, UK: Cambridge University Press, 15–40.
108.
Twigger-RossClareBonaiutoMarinoBreakwellGlynis (2003), “Identity Theories and Environmental Psychology,” in Psychological Theories for Environmental Issues, Mirilia Bonnes, Terence Lee, and Marino Bonaiuto, eds. Farnham, UK: Ashgate, 203–34.
109.
UzzellDavidPolEnricBadenasDavid (2002), “Place Identification, Social Cohesion, and Environmental Sustainability,” Environment and Behavior, 34 (1), 26–53.
110.
VezzaliLorisCadamuroAlessiaVersariAnnalisaGiovanniniDinoTrifilettiElena (2015), “Feeling Like a Group After a Natural Disaster: Common Ingroup Identity and Relations with Outgroup Victims Among Majority and Minority Young Children,” British Journal of Social Psychology, 54 (3), 519–38.
111.
WangXueruiMcCallumAndrewWeiXing (2007), “Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval,” in Seventh IEEE International Conference on Data Mining (ICDM '07), IEEE, https://ieeexplore.ieee.org/document/4470313.
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.
