Engineering:User review

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Short description: Consumer appraisal posted online
A user review of a product on Amazon.com

A user review is a review conducted by any person who has access to the internet and publishes their experience to a review site or social media platform following product testing or the evaluation of a service.[1] User reviews are commonly provided by consumers who volunteer to write the review, rather than professionals who are paid to evaluate the product or service. User reviews might be compared to professional nonprofit reviews from a consumer organization, or to promotional reviews from an advertiser or company marketing a product. Growth of social media platforms has enabled the facilitation of interaction between consumers after a review has been placed on online communities such as blogs, internet forums or other popular platforms.[1]

Purpose of user reviews

User reviews guide stakeholders, including consumers, producers, and competitors decision making process regarding the good or service experienced by the user providing the review.[2] Purchase decisions can be made with easy access to product information through reviews from users who have knowledge from an experience, information or tangible good.[3] Producers of goods and services can utilise user reviews through word of mouth (WOM) recognition enhancing their reputation, but can also be disparaged.[3] For goods which value is derived from knowledge and information, user reviews provide a "wealth of experience information," and therefore increase potential consumers.[4]

Economic effect

In some markets, user reviews are considered more trustworthy than professional or firm initiated marketing.[1]

Consumer

Through user reviews, consumers seeking to make a purchase decision are able to independently analyse and evaluate their choices.[2] Consumers are able to identify with specific product attributes that provide the highest utility by comparing their own value chain with users who provide information about their personal experience.[4] Through the online network, consumers positive interpretation of a user review is likely to increase the chance of purchase, whereas negative interpretation of a user review is likely to broaden the consumers search.[5]

Producer

User reviews are seen as a 'driving force' in marketing, in direct correlation with sales of a good or service.[6] Positive user reviews of a good or service are likely to increase demand of the product through positive attitudes and behaviour toward the company.[6] Research has shown that negative user reviews have a more widespread impact than positive.[4] Both the volume and valence of reviews are recorded to impact demand of goods and services but serve as an opportunity for improvement for management and production chains.[6]

Competitor

By interpreting user reviews, competitors are able to understand their competition's strengths and weaknesses from a user's perspective. The facilitation of distribution of personal experience through user reviews provides an advantageous opportunity for competitors to improve their own product based on their competitors feedback.[7] By providing personal experiences, user reviews give the market a chance to analyse their weaknesses and use it as an opportunity, sometimes at the dispense of the company originally reviewed.[7]

Fake reviews

Advertisers, marketers, and other competitive stakeholders have motivation to produce fake positive user reviews for products they wish to promote or fake negative user reviews for products which they wish to disparage.[8][9] In a fake user review, an actor will create a user account based on some marketing persona and post a user review purporting to be a real person with the traits of the persona.[8] Marketing companies who sell fake reviews train workers to write them in realistic ways and to post them from multiple accounts in order to increase credibility.[10] This is a misuse of the user review system, which universally only invite reviews from typical users and not paid fake personalities.[8] Alternatively, a real user may provide a fake review of a good or service they have not experienced.[11]

A 2021 study from University of California, Los Angeles documented large markets where sellers on Amazon purchase fake reviews in private Facebook groups. These reviews increase the ratings and sales of products and are widely used by sellers.[12]

One way to prevent fake reviews is to create barriers which favor long-term identified users who understand and support community rules in a review site.[8] Amazon is suing fake reviewers.[13] By providing boundaries for membership such as knowing the user's details, or having to pay for membership, companies can provide boundaries.[7]

In 2016, the Australian Competition & Consumer Commission fined Electrodry $215,000 for inciting its franchisees to forge false online reviews to boost their rating on online review websites.[14]

Evaluation of user reviews

Various systems have been proposed to evaluate the quality of user reviews so that consumers can access the best ones, avoid lower quality ones, and prevent mixing of honestly provided reviews with less honest reviews from advertisers or people with an agenda other than nonpartial evaluation.[15]

Consumers perceive user reviews using good grammar and persuasive writing style to be of higher quality than those written in other ways.[16]

The relationship between user reviews and the quality of a product is uncertain.[17] For some levels of quality in some circumstances, there may be no relationship between quality and ratings.[17] For top levels of quality, one study found that user ratings matched scientific ratings a little more than half the time.[17] Furthermore, people reading user reviews tend to perceive them to be as objective as scientific testing, especially when there is an average user review score.[18]

Given a large set of multiple user reviews by different people, there are text analytics algorithms which can accurately predict which reviews come from the same individual authors.[19]

Sentiment analysis can be used to predict the extent to which a review is favorable or critical.[20][21]

Motivations for contributing a user review

Research suggests that motivation to provide a user review commonly stems from psychological attitudes and behaviour.[22] Uses and gratifications theory is a discipline which considers why anyone would volunteer time to create a user review.[23] Some researchers suggest that internal behaviours that value social benefits, self-enhancement, concern for others and the need for gratification are more likely to provide user reviews.[22] Providing a user review is suggested to fulfil a sense of belonging by conforming to beliefs of a majority or minority opinion of personal experience.[22]

Review bombing is when user reviews are made en masse in order to more strongly influence the creator of a product or its sales, in response to an actual or perceived slight against the customers [citation needed]. In some situations, research suggests that competitors take advantage of anonymous review systems to negatively influence and control the intensity of their competition.[24]

Case studies

Many researchers have profiled user reviews on Yelp.[25]

Research has shown that user reviews often influence consumer purchases in the hospitality industry.[26]

User reviews have created criticism and questioning of health care practices, when before the advent of user reviews, health care providers were rarely criticized or evaluated by users.[27]

See also

References

  1. ↑ 1.0 1.1 1.2 Yi, Cheng; Jiang, Zhenhui; Li, Xiuping; Lu, Xianghua (2019). "Leveraging User-Generated Content for Product Promotion: The Effects of Firm-Highlighted Reviews". Information Systems Research 30 (3): 711–725. doi:10.1287/isre.2018.0807. https://pubsonline.informs.org/doi/10.1287/isre.2018.0807. Retrieved October 30, 2020. 
  2. ↑ 2.0 2.1 Goes, Paulo B; Lin, Minfeng; Au Yeung, Ching-man (June 2014). ""Popularity Effect" in User-Generated Content: Evidence from Online Product Reviews". Information Systems Research 25 (2): 222–238. doi:10.1287/isre.2013.0512. https://www.jstor.org/stable/24700171. Retrieved October 30, 2020. 
  3. ↑ 3.0 3.1 Forman, Chris; Ghose, Adindya; Wissenfeld, Batia (September 2008). "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets". Information Systems Research 19 (3): 291–313. doi:10.1287/isre.1080.0193. https://www.jstor.org/stable/23015450. Retrieved October 30, 2020. 
  4. ↑ 4.0 4.1 4.2 Galbreth, Michael R; Ghosh, Bikram P; Pekgun, Pelin (April 20, 2017). "How Unequal Perceptions of User Reviews Impact Price Competition". Decision Sciences 49 (2): 250–275. doi:10.1111/deci.12273. https://onlinelibrary.wiley.com/doi/abs/10.1111/deci.12273. Retrieved 30 October 2020. 
  5. ↑ Wu, Chunhua; Che, Hai; Chan, Tat Y; Lu, Xianghua (September 2015). "The Economic Value of Online Reviews". Marketing Science 34 (5): 739–754. doi:10.1287/mksc.2015.0926. https://www.researchgate.net/publication/283099825. Retrieved October 30, 2020. 
  6. ↑ 6.0 6.1 6.2 Maslowska, Ewa; Malthouse, Edward C; Bernritter, Stefan F (August 25, 2016). "The Effect of Online Customer Reviews' Characteristics on Sales". Advances in Advertising Research (Vol. VII). 7. pp. 87–100. doi:10.1007/978-3-658-15220-8_8. ISBN 978-3-658-15219-2. https://link.springer.com/chapter/10.1007/978-3-658-15220-8_8. Retrieved October 30, 2020. 
  7. ↑ 7.0 7.1 7.2 Duan, Wenjing; Zhou, Wenqi (June 17, 2016). "Do Professional Reviews Affect Online User Choices Through User Reviews? An Empirical Study". Journal of Management Information Systems 33 (1): 202–228. doi:10.1080/07421222.2016.1172460. https://www.duq.edu/Documents/business/faculty/publications/2_Manuscript_JMIS_Zhou.pdf. Retrieved 30 October 2020. 
  8. ↑ 8.0 8.1 8.2 8.3 Ott, Myle; Cardie, Claire; Hancock, Jeff (2012). "Estimating the prevalence of deception in online review communities". Proceedings of the 21st international conference on World Wide Web - WWW '12. pp. 201. doi:10.1145/2187836.2187864. ISBN 9781450312295. 
  9. ↑ Glazer, Jacob; Herrera, Helios; Perry, Motty (2020). "Fake Reviews" (in en). The Economic Journal 131 (636): 1772–1787. doi:10.1093/ej/ueaa124. https://academic.oup.com/ej/advance-article/doi/10.1093/ej/ueaa124/5942863. 
  10. ↑ Cramer, Maria (25 January 2023). "Five Stars, Zero Clue: Fighting the 'Scourge' of Fake Online Reviews". https://www.nytimes.com/2023/01/25/travel/fake-review-investigators.html. 
  11. ↑ Lappas, Theodoros; Sabnis, Guarav; Valkanas, Georgios (2016). "The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry". Information Systems Research 27 (4): 1–38. doi:10.1287/isre.2016.0674. http://pdfs.semanticscholar.org/8bc1/d7198b053849b2cebc6b9ecf19a840d94d39.pdf. Retrieved October 30, 2020. 
  12. ↑ He, Sherry; Hollenbeck, Brett; Proserpio, Davide (25 February 2022). "The Market for Fake Reviews". Marketing Science 41 (5): 896–921. doi:10.1287/mksc.2022.1353. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3664992. 
  13. ↑ Eamonn Fingleton. "After Amazon's Crackdown, Will The Fake Customer Review Industry Just Move Offshore?". Forbes. https://www.forbes.com/sites/eamonnfingleton/2015/10/18/after-amazons-crackdown-will-the-fake-customer-review-industry-just-move-offshore. 
  14. ↑ Anna Trist (14 July 2016). "False Testimonials and Reviews - Learning From One Franchisors Mistake | Lexology" (in en). https://www.lexology.com/library/detail.aspx?g=45d4f1af-a8a1-40f9-8d01-20605fc868d2. 
  15. ↑ Lappas, Theodoros; Terzi, Evimaria (2011). "Toward a Fair Review-Management System". Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. 6912. pp. 293–309. doi:10.1007/978-3-642-23783-6_19. ISBN 978-3-642-23782-9. 
  16. ↑ Otterbacher, Jahna (2011). "Being Heard in Review Communities: Communication Tactics and Review Prominence". Journal of Computer-Mediated Communication 16 (3): 424–444. doi:10.1111/j.1083-6101.2011.01549.x. ISSN 1083-6101. 
  17. ↑ 17.0 17.1 17.2 de Langhe, Bart; Fernbach, Phil; Lichtenstein, Donald R. (4 July 2016). "High Online User Ratings Don't Actually Mean You're Getting a Quality Product". Harvard Business Review. https://hbr.org/2016/07/high-online-user-ratings-dont-actually-mean-youre-getting-a-quality-product. Retrieved 6 July 2016. 
  18. ↑ de Langhe, Bart; Fernbach, Philip M.; Lichtenstein, Donald R. (April 2016). "Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings". Journal of Consumer Research 42 (6): 817–833. doi:10.1093/jcr/ucv047. 
  19. ↑ Almishari, Mishari; Tsudik, Gene (2012). "Exploring Linkability of User Reviews". Computer Security – ESORICS 2012. Lecture Notes in Computer Science. 7459. pp. 307–324. doi:10.1007/978-3-642-33167-1_18. ISBN 978-3-642-33166-4. 
  20. ↑ Duan, Wenjing; Cao, Qing; Yu, Yang; Levy, Stuart (2013). "Mining Online User-Generated Content: Using Sentiment Analysis Technique to Study Hotel Service Quality". 2013 46th Hawaii International Conference on System Sciences. pp. 3119–3128. doi:10.1109/HICSS.2013.400. ISBN 978-1-4673-5933-7. 
  21. ↑ Yatani, Koji; Novati, Michael; Trusty, Andrew; Truong, Khai N. (2011). "Review spotlight". Proceedings of the 2011 annual conference on Human factors in computing systems - CHI '11. pp. 1541. doi:10.1145/1978942.1979167. ISBN 9781450302289. 
  22. ↑ 22.0 22.1 22.2 Beldad, Ardion; Voutsas, Charalampos (June 26, 2018). "Understanding the Motivation to Write Reviews for Mobile Apps among German Users: Testing the Expanded Theory of Planned Behaviour Using a Structural Equation Modeling Approach". Journal of Technology in Behavioral Science 3 (4): 301–311. doi:10.1007/s41347-018-0063-5. PMID 30547081. 
  23. ↑ Hicks, Amy; Comp, Stephen; Horovitz, Jeannie; Hovarter, Madeline; Miki, Maya; Bevan, Jennifer L. (2012). "Why people use Yelp.com: An exploration of uses and gratifications". Computers in Human Behavior 28 (6): 2274–2279. doi:10.1016/j.chb.2012.06.034. ISSN 0747-5632. 
  24. ↑ Zhu, Feng; Zhang, Xiaoquan (March 2010). "Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics". Journal of Marketing 74 (2): 133–148. doi:10.1509/jm.74.2.133. https://blog.mikezhang.com/files/gamereviews.pdf. Retrieved October 30, 2020. 
  25. ↑ Luca, Michael (2011). "Reviews, Reputation, and Revenue: The Case of Yelp.Com". SSRN Working Paper Series. doi:10.2139/ssrn.1928601. ISSN 1556-5068. http://www.hbs.edu/faculty/pages/download.aspx?name=12-016.pdf. 
  26. ↑ Ong, Beng Soo (2012). "The Perceived Influence of User Reviews in the Hospitality Industry". Journal of Hospitality Marketing & Management 21 (5): 463–485. doi:10.1080/19368623.2012.626743. ISSN 1936-8623. 
  27. ↑ Hardey, M. (2010). "Consuming Professions: User-review websites and health services". Journal of Consumer Culture 10 (1): 129–149. doi:10.1177/1469540509355023. ISSN 1469-5405. 



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