Business & Information Systems Engineering

, Volume 5, Issue 3, pp 141–151 | Cite as

The Recipe for the Perfect Review?

An Investigation into the Determinants of Review Helpfulness
  • Michael Scholz
  • Verena Dorner
Research Paper


Online product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools.


Electronic commerce Product reviews Internet retailing Electronic word-of-mouth 

Supplementary material

12599_2013_259_MOESM1_ESM.pdf (50 kb)
(PDF 50 kB)


  1. Benamara F, Cesarano C, Picariello A, Reforgiato D, Subrahmanian VS (2007) Sentiment analysis: adjectives and adverbs are better than adjectives alone. In: Proceedings of the 1st international AAAI conference on weblogs and social media, Boulder, CO Google Scholar
  2. Berger J, Sorensen AT, Rasmussen SJ (2010) Positive effects of negative publicity: when negative reviews increase sales. Marketing Sci 29(5):815–827 CrossRefGoogle Scholar
  3. Bone PF (1995) Word-of-mouth effects on short-term and long-term product judgments: interpersonal buyer behavior in marketing. J Bus Res 32(3):213–223 CrossRefGoogle Scholar
  4. Burton J, Khammash M (2010) Why do people read reviews posted on consumer-opinion portals? J Mark Manag 26(3):230–255 CrossRefGoogle Scholar
  5. Cacioppo JT, Petty RE (1984) The elaboration likelihood model of persuasion. Adv Consum Res 11:673–675 Google Scholar
  6. Chen CC, Tseng Y (2011) Quality evaluation of product reviews using an information quality framework. Decis Support Syst 50(4):755–768 CrossRefGoogle Scholar
  7. Chen P, Dhanasobhon S, Smith MD (2008) All reviews are not created equal: the disaggregate impact of reviews and reviewers at SSRN working paper. Accessed 2013-02-25
  8. Connors L, Mudambi SM, Schuff D (2011) Is it the review or the reviewer? A multi-method approach to determine the antecedents of online review helpfulness. In: Proceedings of the 44th Hawaii international conference on systems science (HICSS), Hawaii, USA Google Scholar
  9. Danescu-Niculescu-Mizil C, Kossinets G, Kleinberg JM, Lee L (2009) How opinions are received by online communities: a case study on helpfulness votes. In: Quemada J, León G, Maarek Y, Nejdl W (eds) Proceedings of the 18th international conference on world wide web. ACM, New York Google Scholar
  10. Dellarocas C (2003) The digitization of word of mouth: promise and challenges of online feedback mechanisms. Manag Sci 49(10):1407–1424 CrossRefGoogle Scholar
  11. Dellarocas C, Gao G, Narayan R, (2010) Are consumers more likely to contribute online reviews for hit or Niche products? J Man Inf Sys 27(2):127–157 CrossRefGoogle Scholar
  12. Eagly AH (1974) Comprehensibility of persuasive arguments as a determinant of opinion change. J Pers Soc Psychol 29(6):758–773 CrossRefGoogle Scholar
  13. Eagly AH, Chaiken S (1984) Cognitive theories of persuasion. In: Berkowitz L (ed) Advances in experimental social psychology. Academic Press, San Diego, pp 267–359 Google Scholar
  14. Feldman JM, Lynch JG (1988) Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. J Appl Psychol 73(3):421–435 CrossRefGoogle Scholar
  15. Folkes VS (1988) Recent attribution research in consumer behavior: a review and new directions. J Consum Res 14(4):548–565 CrossRefGoogle Scholar
  16. Ford GT, Smith DB, Swasy JL (1990) Consumer skepticism of advertising claims: testing hypotheses from economics of information. J Consum Res 16(4):433–441 CrossRefGoogle Scholar
  17. Forman C, Ghose A, Wiesenfeld B (2008) Examining the relationship between reviews and sales: the role of reviewer identity disclosure in electronic markets. Inf Syst Res 19(3):291–313 CrossRefGoogle Scholar
  18. Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans Knowl Data Eng 23(10):1498–1512 CrossRefGoogle Scholar
  19. Greene WH (2012) Econometric analysis, 7th edn. Prentice Hall, Upper Saddle River Google Scholar
  20. Hao Y, Li Y, Zou P (2009) Why some online product reviews have no usefulness rating? In: Proceedings of the Pacific Asia conference on information systems (PACIS 2009), Hyderabad, India, Paper 100 Google Scholar
  21. Herr PM, Kardes FR, Kim J (1991) Effects of word-of-mouth and product-attribute information on persuasion: an accessibility-diagnosticity perspective. J Consum Res 17(4):454–462 CrossRefGoogle Scholar
  22. Jain SP, Posavac SS (2001) Prepurchase attribute verifiability, source credibility, and persuasion. J Consum Psychol 11(3):169–180 CrossRefGoogle Scholar
  23. Jin J, Liu Y (2010) How to interpret the helpfulness of online product reviews: bridging the needs between customers and designers. In: Cortizo JC, Carrero FM, Cantador I, Troyano JA, Rosso P (eds) Proceedings of the 2nd international workshop on search and mining user-generated contents. ACM, New York, pp 87–94 CrossRefGoogle Scholar
  24. Kim S, Pantel P, Chklovski T, Pennacchiotti M (2006) Automatically assessing review helpfulness. In: Proceedings of the 2006 conference on empirical methods in natural language processing. Association for computational linguistics. Sydney, Australia, pp 423–430 Google Scholar
  25. Klare GR (2000) The measurement of readability: useful information for communicators. ACM J Comput Doc 24(3):11–25 Google Scholar
  26. Korfiatis N, Rodríguez D, Sicilia M (2008) The impact of readability on the usefulness of online product reviews: a case study on an online bookstore. In: Lytras MD, Carroll JM, Damiani E, Tennyson RD (eds) Emerging technologies and information systems for the knowledge society. Springer, Heidelberg, pp 423–432 CrossRefGoogle Scholar
  27. Lahiri S, Mitra P, Lu X (2011) Informationality judgement at sentence level and experiments with formality score. In: Proceedings of the 12th international conference on computational linguistics and intelligent text processing, Tokio Google Scholar
  28. Li MX, Huang L, Tan CH, Wei KK (2011) Assessing the helpfulness of online product review: a progressive experimental approach. In: Seddon PB, Gregor S (eds) Proceedings of the Pacific Asia conference on information systems (PACIS 2011), Brisbane, Australia, Paper 111 Google Scholar
  29. Li X, Hitt LM (2008) Self-selection and information role of online product reviews. Inf Syst Res 19(4):456–474 CrossRefGoogle Scholar
  30. Liu J, Cao Y, Lin C, Huang Y, Zhou M (2007) Low-quality product review detection in opinion summarization. In: Proceedings of the joint conference on empirical methods in natural language processing and computational natural language learning (EMN-CoNLL), pp 334–342 Google Scholar
  31. Liu Y, Huang X, An A, Yu X (2008) HelpMeter: a nonlinear model for predicting the helpfulness of online reviews. In: Proceedings of the 2008 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, Sydney, Australia, pp 793–796 CrossRefGoogle Scholar
  32. McLaughlin HG (1969) SMOG grading – a new readability formula. J Read 12(8):639–646 Google Scholar
  33. Mudambi SM, Schuff D (2010) What makes a helpful online review? A study of customer reviews on MIS Q 34(1):185–200 Google Scholar
  34. Nelson P (1970) Information and consumer behavior. J Polit Econ 78(20):311–329 CrossRefGoogle Scholar
  35. Netzer O, Srinivasan V (2011) Adaptive self-explication of multiattribute preferences. J Mark Res 48(1):140–156 Google Scholar
  36. Otterbacher J (2008) Managing information in online product review communities: two approaches. In: Golden W, Acton T, Conboy K, van der Heijden H, Tuunainen VK (eds) Proceedings of the 16th European conference on information systems, pp 706–717 Google Scholar
  37. Pan Y, Zhang JQ (2011) Born unequal: a study of the helpfulness of user-generated product reviews. J Retail 87(4):598–612 CrossRefGoogle Scholar
  38. Park DY, Lee J, Han I (2007) The effect of on-line consumer reviews on consumer purchasing intention: the moderating role of involvement. Int J Electron Commer 11(4):125–148 CrossRefGoogle Scholar
  39. Schindler RM, Bickart B (2012) Perceived helpfulness of online consumer reviews: the role of message content and style. J Consum Behav 11:234–243 CrossRefGoogle Scholar
  40. Schlosser AE (2011) Can including pros and cons increase the helpfulness and persuasiveness of online reviews? The interactive effects of ratings and arguments. J Consum Psychol 21(3):226–239 Google Scholar
  41. Scholz SW, Meissner M, Decker R (2010) Measuring consumer preferences for complex products: a compositional approach based on paired comparisons. J Mark Res 47(4):685–698 CrossRefGoogle Scholar
  42. Schwenk CR (1986) Information, cognitive biases, and commitment to a course of action. Acad Manag Rev 11(2):298–310 Google Scholar
  43. Sen S, Lerman D (2007) Why are you telling me this? An examination into negative consumer reviews on the web. J Interact Mark 21(4):76–94 CrossRefGoogle Scholar
  44. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423 Google Scholar
  45. Toutanova K, Manning CD (2000) Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In: Proceedings of the joint SIGDAT conference on empirical methods in natural language processing and very large corpora, Hong Kong, China Google Scholar
  46. Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185(4157):1124–1131 CrossRefGoogle Scholar
  47. Wang B, Zhu W, Chen L (2011) Improving the Amazon review system by exploiting the credibility and time-decay of public reviews. Informatica 35(4):463–472 Google Scholar
  48. Wang RW, Strong DM (1996) Beyond accuracy: what data quality means to data consumers. J Manag Inf Syst 12(4):5–33 Google Scholar
  49. Weathers D, Sharma S, Wood SL (2007) Effects of online communication practices on consumer perceptions of performance uncertainty for search and experience goods. J Retail 83(4):393–401 CrossRefGoogle Scholar
  50. Wright DB, London K (2009) Modern regression techniques using R. Sage, London Google Scholar
  51. Wu P, van der Heijden H, Korfiatis N (2011) The influences of negativity and review quality on the helpfulness of online reviews. In: Galletta DF, Liang T (eds) Proceedings of the international conference on information systems, Shanghai, China Google Scholar
  52. Xia L, Bechwati NN (2011) Word of mouse: the role of cognitive personalization in online consumer reviews. J Interactive Advertising 9(1):3–13 Google Scholar
  53. Xia R, Zong C, Li S (2011) Ensemble of feature sets and classification algorithms for sentiment classification. Inf Sci 181(6):1138–1152 CrossRefGoogle Scholar
  54. Zhang JQ, Craciun G, Shin D (2010) When does electronic word-of-mouth matter? A study of consumer product reviews. J Bus Res 63(12):1336–1341 CrossRefGoogle Scholar
  55. Zhang R, Tran T (2010) Helpful or unhelpful: a linear approach for ranking product reviews. Electron Commer Res 11(3):220–230 Google Scholar
  56. Zhang R, Tran T (2011) An information gain-based approach for recommending useful product reviews. Knowl Inf Syst 26(3):419–434 CrossRefGoogle Scholar
  57. Zhang Z, Varadarajan B (2006) Utility scoring of product reviews. In: Yu PS, Tsotras V, Fox EA, Liu B (eds) Proceedings of the 15th ACM international conference on information and knowledge management, Arlington, VA. ACM, New York, p 51 Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden 2013

Authors and Affiliations

  1. 1.Juniorprofessur für E-CommerceUniversity PassauPassauGermany
  2. 2.Chair of Business Computing IIUniversity PassauPassauGermany

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