Analysis of Review Helpfulness Based on Consumer Perspective

Yuanlin Chen; Yueting Chai; Yi Liu; Yang Xu

doi:10.1109/TST.2015.7128942

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Open Access

Analysis of Review Helpfulness Based on Consumer Perspective

Yuanlin Chen, Yueting Chai(

), Yi Liu, Yang Xu

National Engineering Laboratory for E-Commerce Technologies, Tsinghua University, Beijing 100084, China.

DNSLAB, China Internet Network Information Center, Beijing 100190, China

Show Author Information

Abstract

When consumers make purchase decisions, they generally refer to the reviews generated by other consumers who have already purchased similar products in order to get more information. Online transaction platforms provide a highly convenient channel for consumers to generate and retrieve product reviews. In addition, consumers can also vote reviews perceived to be helpful in making their decision. However, due to diverse characteristics, consumers can have different preferences on products and reviews. Their voting behavior can be influenced by reviews and existing review votes. To explore the influence mechanism of the reviewer, the review, and the existing votes on review helpfulness, we propose three hypotheses based on the consumer perspective and perform statistical tests to verify these hypotheses with real review data from Amazon. Our empirical study indicates that review helpfulness has significant correlation and trend with reviewers, review valance, and review votes. In this paper, we also discuss the implications of our findings on consumer preference and review helpfulness.

Keywords

consumer preference online decision making review helpfulness behavior analysis

References

[1]

Sundaram,

D. S.

Mitra,

and Webster,

Word-of-mouth communications: A motivational analysis, Advances in Consumer Research, vol. 25, pp. 527-531, 1998.

Google Scholar

[2]

Kotler

and Kotler,

Marketing Management. Upper Saddle River, NJ, USA: Prentice Hall, 2005.

[3]

Dhar

and Chang,

E. A.

Does chatter matter?

The impact of user-generated content on music sales, Journal of Interactive Marketing, vol. 23, pp. 300-307, 2009.

Crossref Google Scholar

[4]

Ye,

Law,

Gu,

and Chen,

The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings, Computers in Human Behavior, vol. 27, pp. 634-639, 2011.

Crossref Google Scholar

[5]

Forman,

Ghose,

and Wiesenfeld,

Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets, Information Systems Research, vol. 19, pp. 291-313, 2008.

Crossref Google Scholar

[6]

Hu,

Liu,

and Zhang,

J. J.

Do online reviews affect product sales?

The role of reviewer characteristics and temporal effects, Information Technology and Management, vol. 9, pp. 201-214, 2008.

Crossref Google Scholar

[7]

Chevalier

J. A.

and Mayzlin,

The effect of word of mouth on sales: Online book reviews, Journal of Marketing Research, vol. 43, pp. 345-354, 2006.

Crossref Google Scholar

[8]

Liu,

Word of mouth for movies: Its dynamics and impact on box office revenue, Journal of Marketing, vol. 70, pp. 74-89, 2006.

Crossref Google Scholar

[9]

Park,

D.-H.

Lee,

and Han,

The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement, International Journal of Electronic Commerce, vol. 11, pp. 125-148, 2007.

Crossref Google Scholar

[10]

Wu,

P. F.

Van der Heijden,

and Korfiatis,

The influences of negativity and review quality on the helpfulness of online reviews, presented in International Conference on Information Systems, Shanghai, China, 2011.

[11]

Skowronski

J. J.

and Carlston,

D. E.

Negativity and extremity biases in impression formation: A review of explanations, Psychological Bulletin, vol. 105, p. 131, 1989.

Crossref Google Scholar

[12]

Herr,

P. M.

Kardes,

F. R.

and Kim,

Effects of word-of-mouth and product-attribute information on persuasion: An accessibility-diagnosticity perspective, Journal of Consumer Research, vol. 17, no. 4, pp. 454-462, 1991.

Crossref Google Scholar

[13]

Zhang,

J. Q.

Craciun,

and Shin,

When does electronic word-of-mouth matter?

A study of consumer product reviews, Journal of Business Research, vol. 63, pp. 1336-1341, 2010.

Crossref Google Scholar

[14]

McAuley

and Leskovec,

Hidden factors and hidden topics: Understanding rating dimensions with review text, in Proc. 7th ACM Conference on Recommender Systems, Hong Kong, China, 2013, pp. 165-172.

Crossref

[15]

Hennig-Thurau,

Gwinner,

K. P.

Walsh,

and Gremler,

D. D.

Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet?

Journal of Interactive Marketing, vol. 18, pp. 38-52, 2004.

Crossref Google Scholar

[16]

Anderson,

E. W.

The formation of market-level expectations and its covariates, Journal of Consumer Research, vol. 30, pp. 115-124, 2003.

Crossref Google Scholar

[17]

Bone,

P. F.

Word-of-mouth effects on short-term and long-term product judgments, Journal of Business Research, vol. 32, pp. 213-223, 1995.

Crossref Google Scholar

[18]

Dichter,

How word-of-mouth advertising works, Harvard Business Review, vol. 44, pp. 147-160, 1966.

Google Scholar

[19]

Anderson,

E. W.

Customer satisfaction and word of mouth, Journal of Service Research, vol. 1, pp. 5-17, 1998.

Crossref Google Scholar

[20]

Chung

C. M.

and Darke,

P. R.

The consumer as advocate: Self-relevance, culture, and word-of-mouth, Marketing Letters, vol. 17, pp. 269-279, 2006.

Crossref Google Scholar

[21]

Tong,

Wang,

X. W.

Tan,

C. H.

and Teo,

H. H.

An empirical study of information contribution to online feedback systems: A motivation perspective, Information & Management, vol. 50, pp. 562-570, 2013.

Crossref Google Scholar

[22]

Nelson,

Information and consumer behavior, The Journal of Political Economy, vol. 78, no. 2, pp. 311-329, 1970.

Crossref Google Scholar

[23]

Tversky

and Kahneman,

Judgment under uncertainty: Heuristics and biases, Science, vol. 185, pp. 1124-1131, 1974.

Crossref Google Scholar

[24]

Mudambi

S. M.

and Schuff,

What makes a helpful review?

A study of customer reviews on Amazon. com, MIS Quarterly, vol. 34, pp. 185-200, 2010.

Crossref Google Scholar

[25]

Pan

and Zhang,

J. Q.

Born unequal: A study of the helpfulness of user-generated product reviews, Journal of Retailing, vol. 87, pp. 598-612, 2011.

Crossref Google Scholar

[26]

Liu,

Huang,

An,

and Yu,

Modeling and predicting the helpfulness of online reviews, in Proc. Eighth IEEE International Conference on Data Mining, Pisa, Italy, 2008, pp. 443-452.

Crossref

[27]

Pavlou

P. A.

and Dimoka,

The nature and role of feedback text comments in online marketplaces: Implications for trust building, price premiums, and seller differentiation, Information Systems Research, vol. 17, pp. 392-414, 2006.

Crossref Google Scholar

[28]

Ghose

and Ipeirotis,

P. G.

Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics, Knowledge and Data Engineering, IEEE Transactions on, vol. 23, pp. 1498-1512, 2011.

Crossref Google Scholar

[29]

Korfiatis,

Garcła-Bariocanal,

and Snchez-Alonso,

Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content, Electronic Commerce Research and Applications, vol. 11, pp. 205-217, 2012.

Crossref Google Scholar

[30]

Danescu-Niculescu-Mizil,

Kossinets,

Kleinberg,

and Lee,

How opinions are received by online communities: A case study on amazon. com helpfulness votes, in Proc. 18th International Conference on World Wide Web, 2009, pp. 141-150.

[31]

Simon,

H. A.

The New Science of Management Decision. New York, NY, USA: Harper & Brothers,1960.

Crossref

[32]

Kohli,

Devaraj,

and Mahmood,

M. A.

Understanding determinants of online consumer satisfaction: A decision process perspective, Journal of Management Information Systems, vol. 21, pp. 115-136, 2004.

Crossref Google Scholar

[33]

Dellarocas,

The digitization of word of mouth: Promise and challenges of online feedback mechanisms, Management Science, vol. 49, pp. 1407-1424, 2003.

Crossref Google Scholar

[34]

Feldman

J. M.

and Lynch,

J. G.

Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior, Journal of Applied Psychology, vol. 73, p. 421, 1988.

Crossref Google Scholar

[35]

Clemons,

E. K.

Gao,

G. G.

and Hitt,

L. M.

When online reviews meet hyperdifferentiation: A study of the craft beer industry, Journal of Management Information Systems, vol. 23, pp. 149-171, 2006.

Crossref Google Scholar

[36]

Dellarocas,

Awad,

and Zhang,

Exploring the value of online reviews to organizations: Implications for revenue forecasting and planning, in Proc. International Conference on Information Systems,Washington DC, USA, 2004.

[37]

Ahluwalia,

How prevalent is the negativity effect in consumer environments?

Journal of Consumer Research, vol. 29, pp. 270-279, 2002.

Crossref Google Scholar

[38]

Kanouse

D. E.

and Hanson Jr,

L. R.

Negativity in evaluations, in Attribution: Perceiving the Causes of Behavior. Hillsdale,

, USA: Lawrence Erlbaum Associates, Inc, 1987, PP. 47-62.

[39]

Hamed,

K. H.

Trend detection in hydrologic data: The Mann-Kendall trend test under the scaling hypothesis, Journal of Hydrology, vol. 349, pp. 350-363, 2008.

Crossref Google Scholar

Tsinghua Science and Technology

Volume 20 Issue 3,
June 2015

Pages 293-305

DOI: 10.1109/TST.2015.7128942

Cite this article:

Chen Y, Chai Y, Liu Y, et al. Analysis of Review Helpfulness Based on Consumer Perspective. Tsinghua Science and Technology, 2015, 20(3): 293-305. https://doi.org/10.1109/TST.2015.7128942

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Received: 12 February 2015

Revised: 07 April 2015

Accepted: 15 April 2015

Published: 19 June 2015