As social media and virtual communities increase in popularity, the spread of word of mouth becomes easier, challenging firms to measure and manage the success of marketing initiatives in online community environments. This research examines how consumers react to firms' active participation in consumer-to-consumer conversations in an online community setting. The authors develop a tailored community-matched measure of consumer reaction (consumer sentiment) and analyze more than 115,000 consumer posts from ten online forums with active firm participation. The results indicate that consumers show diminishing returns to active firm engagement, which, at very high levels, can undermine consumer sentiment. Further subgroup analyses by conversation type indicate that these relationships hold for conversations that address consumers' functional needs but do not hold for conversations that address social needs. Finally, the results show diminishing returns to firm engagement for consumers primarily interested in product-related support but show no relationship for consumers primarily interested in inspiration and entertainment. These findings provide insights for marketing performance measurement and resource allocation in online communities.
AntweilerAntweiler and FrankMurray Z. (2004), “Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards,”Journal of Finance, 59 (3), 1259–94.
2.
ArellanoAndrew (1987), “Computing Robust Standard Errors for Within-Groups Estimators,”Oxford Bulletin of Economics & Statistics, 49 (4), 431–34.
3.
ChenChen and XieJinhong (2008), “Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix,”Management Science, 54 (3), 477–91.
4.
ChevalierJudith A. and MayzlinDina (2006), “The Effect of Word of Mouth on Sales: Online Book Reviews,”Journal of Marketing Research, 43 (August), 345–54.
5.
CuiCui and CurryDavid (2005), “Prediction in Marketing Using the Support Vector Machine,”Marketing Science, 24 (4), 595–615.
6.
DasSanjiv R. and ChenMike Y. (2007), “Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web,”Management Science, 53 (9), 1375–88.
7.
DaveKushalLawrenceSteve and PennockDavid (2003), “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews,” in Proceedings of the 12th International Conference on World Wide Web (WWW-2003).Budapest: Association for Computing Machinery, 519–28.
8.
DellarocasAndrew (2003), “The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms,”Management Science, 49 (10), 1407–1424.
9.
DellarocasAndrew (2006), “Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms,”Management Science, 52 (10), 1577–93.
10.
DengNaiyangTianYingjie and ZhangChunhua (2012), Support Vector Machines: Theory, Algorithms, and Extensions.Boca Raton, FL: CRC Press.
11.
DholakiaUtpal M.BlazevicVeraWiertzCaroline and AlgesheimerRené (2009), “Communal Service Delivery: How Customers Benefit from Participation in Firm-Hosted Virtual P3 Communities,”Journal of Service Research, 12 (2), 208–226.
12.
DurantDurant and SmithMichael (2006), “Mining Sentiment Classification from Political Web Logs,” in Proceedings of Workshop on Web Mining and Web Usage Analysis of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (WebKDD-2006). Philadelphia.
13.
GodesGodes and MayzlinDina (2004), “Using Online Conversations to Study Word-of-Mouth Communication,”Marketing Science, 23 (4), 545–60.
14.
GodesGodes and MayzlinDina (2009), “Firm-Created Word-of-Mouth Communication: Evidence from a Field Test,”Marketing Science, 28 (4), 721–39.
15.
GodfreyAndreaSeidersKathleen and VossGlenn B. (2011), “Enough Is Enough! The Fine Line in Executing Multichannel Relational Communication,”Journal of Marketing, 75 (July), 94–109.
16.
GohKhim-Yong, HengCheng-Suang, and LinZhijie (2013), “Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content,”Information Systems Research, 24 (1), 88–107.
17.
HaansHaans and GijsbrechtsEls (2010), “Sales Drops from Closing Shops: Assessing the Impact of Store Outlet Closures on Retail Chain Revenue,”Journal of Marketing Research, 47 (December), 1025–40.
18.
KeerthiS. SathiyaShevadeShirish K.BhattacharyyaChiranjib and MurthyK.R.K. (2001), “Improvements to Platt's SMO Algorithm for SVM Classifier Design,”Neural Computation, 13 (3), 637–49.
19.
KennedyAndrew (2013), A Guide to Econometrics, 6th ed. Oxford, UK: Wiley-Blackwell.
20.
KieschnickKieschnick and McCulloughB.D. (2003), “Regression Analysis of Variates Observed on (0, 1): Percentages, Proportions and Fractions,”Statistical Modelling, 3 (3), 193–213.
21.
KöhlerClemens F., RohmAndrew J., de RuyterKo, and WetzelsMartin (2011), “Return on Interactivity: The Impact of Online Agents on Newcomer Adjustment,”Journal of Marketing, 75 (March), 93–108.
22.
KumarV., VenkatesanRajkumar and ReinartzWerner (2008), “Performance Implications of Adopting a Customer-Focused Sales Campaign,”Journal of Marketing, 72 (September), 50–68.
23.
MathwickAndrew (2002), “Understanding the Online Consumer: A Typology of Online Relational Norms and Behavior,”Journal of Interactive Marketing, 16 (1), 40–55.
24.
McAlexanderJames H.SchoutenJohn W. and KoenigHarold F. (2002), “Building Brand Community,”Journal of Marketing, 66 (January), 38–54.
25.
MillerAmalia R. and TuckerCatherine (2013), “Active Social Media Management: The Case of Health Care,”Information Systems Research, 24 (1), 52–70.
26.
MoeWendy W. and TrusovMichael (2011), “The Value of Social Dynamics in Online Product Ratings Forums,”Journal of Marketing Research, 48 (June), 444–56.
27.
MuñizAlbert M.Jr., and O'GuinnThomas C. (2001), “Brand Community,”Journal of Consumer Research, 27 (4), 412–32.
28.
NambisanNambisan and WattJames H. (2011), “Managing Customer Experiences in Online Product Communities,”Journal of Business Research, 64 (8), 889–95.
29.
NambisanNambisan and BaronRobert A. (2007), “Interactions in Virtual Customer Environments: Implications for Product Support and Customer Relationship Management,”Journal of Interactive Marketing, 21 (2), 42–62.
30.
NetzerOdedFeldmannRonenGoldenbergJacob and FreskoMoshe (2012), “Mine Your Own Business: Market-Structure Surveillance Through Text Mining,”Marketing Science, 31 (3), 521–43.
31.
NichollsNicholls and SongFei (2010), “Comparison of Feature Selection Methods for Sentiment Analysis,” in Advances in Artificial Intelligence, Lecture Notes in Computer Science, FarzindarA. and KeseljV., eds. Berlin: Springer, 286–89.
32.
PangPang and LeeLillian (2008), “Opinion Mining and Sentiment Analysis,”Foundation and Trends in Information Retrieval, 2 (1/2), 1–135.
33.
PangBo and VaithyanathanShivakumar (2002), “Thumbs Up? Sentiment Classification Using Machine Learning Techniques,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).Philadelphia: Association for Computational Linguistics, 79–86.
34.
PapkeLeslie E. and WooldridgeJeffrey M. (1996), “Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates,”Journal of Applied Econometrics, 11 (6), 619–32.
35.
PapkeLeslie E. and WooldridgeJeffrey M. (2008), “Panel Data Methods for Fractional Response Variables with an Application to Test Pass Rates,”Journal of Econometrics, 145 (1/2), 121–33.
36.
PetersenMitchell A. (2009), “Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches,”Review of Financial Studies, 22 (1), 435–80.
37.
PlattJohn C. (1999), “Fast Training of Support Vector Machines Using Sequential Minimal Optimization,” in Advances in Kernel Methods: Support Vector Learning, SchölkopfBernhard, BurgesChristopher J.C., and SmolaAlexander J., eds. Cambridge, MA: MIT Press.
38.
PorterConstance Elise and DonthuNaveen (2008), “Cultivating Trust and Harvesting Value in Virtual Communities,”Management Science, 54 (1), 113–28.
39.
RishikaRishikaKumarAshishJanakiramanRamkumar and BezawadaRam (2013), “The Effect of Customers' Social Media Participation on Customer Visit Frequency and Profitability: An Empirical Investigation,”Information Systems Research, 24 (1), 108–127.
40.
SchauHope Jensen, MuñizAlbert M., and ArnouldEric J. (2009), “How Brand Community Practices Create Value,”Journal of Marketing, 73 (September), 30–51.
41.
TirunillaiTirunillai and TellisGerard J. (2012), “Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance,”Marketing Science, 31 (2), 198–215.
42.
TirunillaiTirunillai and TellisGerard J. (2014), “Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation,”Journal of Marketing Research, 51 (August), 463–79.
43.
Van DolenWillemijn M., DabholkarPratibha A., and de RuyterKo (2007), “Satisfaction with Online Commercial Group Chat: The Influence of Perceived Technology Attributes, Chat Group Characteristics, and Advisor Communication Style,”Journal of Retailing, 83 (3), 339–58.
44.
VenkatesanVenkatesan and KumarV. (2004), “A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy,”Journal of Marketing, 68 (October), 106–125.
45.
VohraVohra and TeraiyaJay (2013), “A Comparative Study of Sentiment Analysis Techniques,”Journal of Information, Knowledge, and Research in Computer Engineering, 83 (3), 339–58.
46.
WittenIan H.FrankEibe and HallMark A. (2011), Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed. Burlington, MA: Morgan Kaufmann.
47.
WooldridgeJeffrey M. (2010), Econometric Analysis of Cross Section and Panel Data, 2nd ed. Cambridge, MA: MIT Press.
48.
ZhouZhou and ChaovalitPimwadee (2008), “Ontology-Supported Polarity Mining,”Journal of the American Society for Information Science and Technology, 59 (1), 98–110.
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