What are ratings?
Online ratings and reviews have reshaped the way customers make decisions before purchasing any new products or services; almost 93% of customers look at review and ratings before making their decision to purchase (Podium, 2017). Ratings allow quantification of user perception through selection of scores on a ‘rating scale’, and most commonly used scale of measurement is the Likert scale. Ratings help in translating the experience of a customer into a tangible number, which make it easy to comprehend, and can then be used by customers to compare competing sellers or products.
Do ratings impact decision making?
A reliable rating system (is a type of reputational system) gives customers a chance to voice their experience of the counterparties they have engaged with in a transaction, as well as provide insight into the credibility, of the seller and the seller’s product. Linkedin.com report into perception of B2B businesses on value of ratings and reviews mentions that “97% of IT buyers indicate that they rely on peer recommendations, ratings, and reviews when it comes to buying business software” (LinkedIn, 2020). A study by Harvard Business School ‘estimated that a one-star rating increase on Yelp lead to an increase of 5% to 9% in revenues from a restaurant’, and a study at Cornell University found that that same increase was tied to an 11% sway in room rates’ (Pansini, 2013). Among the criteria looked by buyers before making the purchase decision ‘Star Ratings’ is second most important aspect, first being the ‘reviewing content’ on the seller or the product (Podium, 2017).
How do companies tamper ratings?
“A research by Mike and Georgios Zervas has found that businesses are especially likely to engage in review fraud when their reputation is struggling, or competition is particularly intense” (Michael Luca, 2016). With increasing competition in digital space some companies including marketplaces have started resorting to fake rating systems, which deceive customers and general public into making a purchase from a company or of a product with much lower quality than shown through manipulated ratings. Companies selling goods or services sometimes try to tamper ratings on their website or other websites (with low controls) by leaving positive ratings scores on themselves and negative ones for their competitors making it look as if it was left by a genuine customer commonly known as astroturfing, and commercially referred to as rating fraud. Rating fraud is so prevalent, that has been reported that 19 review management companies were caught and fined for dishonest ratings on various sites such as Yelp.com (D, 2014). There are two types of rating frauds based on user ratings — when the companies rate themselves very high is called ‘ballot stuffing’ and when they rate their competitors very low is called ‘bad mouthing’ (Dellarocas, 2000) ‘Companies employ SEO companies to generate fake ratings and reviews on marketplaces (Pansini, 2013), thereby creating a false ‘positive reputation effect’ which goes a long way in influencing buyers’ purchase decision on digital platforms. Some companies have been found to open fake buyer accounts and undertake transactions to build credibility of their ratings on the marketplaces.
How can this issue be solved what are the limitations?
It has been proposed that blockchain technology and similar distributed ledger technologies if employed effectively can offer immutability and genuineness of ratings (Yuanfeng & Zhu, 2016). Verified user accounts should only be allowed to rate sellers who have completed the transaction on the marketplace, even though the rating fraud can occur through this as well but due to increased cost and risk, it acts as a deterrent to companies from using these kind of tactics. But there are still limitations to fully solve this issue, it has been mentioned that ‘Blockchain systems are effective in preventing some types of rating fraud, such as bad mouthing and whitewashing attacks, but they may be unable to prevent ballot stuffing sybil, constant, and camouflage attacks’ (Yuanfeng & Zhu, 2016).
Maalexi.com has a rating system built on the integrity and verifiable authenticity established through distributed ledger technology which allows it to provide corporate badges to sellers (based on analysis of company’s public information), and transactional ratings provided by verified buyers, thereby representing the true transactional experience of the user.
References
Michael Luca, G. Z. (2016, Jan). Retrieved from https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2015.2304?journalCode=mnsc&
Geoff Donaker, Hyunjin KIm, Michael Luca. (2019, December). Designing Better Online Review Systems. Retrieved from Harvard Business Review: https://hbr.org/2019/11/designing-better-online-review-systems
LinkedIn. (2020). LinkedIn and G2 crowd. Retrieved from LinkedIn Business: https://business.linkedin.com/content/dam/me/business/en-us/marketing-solutions/resources/pdfs/linkedin-crowd-b2b-product-review-book.pdf
Pansini, L. (2013). Astroturfing: Fake Online Reviews and How to Spot Them. Retrieved from Rocketmatter: https://www.rocketmatter.com/general/astroturfing-fake-online-reviews/
D, S. (2014). Nineteen companies found guilty of writing fake consumer reviews.,. Retrieved from Heralddeparis.com: http://www.heralddeparis.com/nineteen-companies-found-guilty-of-writing-fake-consumer-reviews/232920
Dellarocas, C. (2000). Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. Proceedings of the 2nd ACM Conference on Electronic commerce, (pp. 150–157). Minnesota.
Yuanfeng, C., & Zhu, D. (2016). Fraud detections for online businesses: a perspective from blockchain technology. Financial Innovation volume.
Podium. (2017). State of Online Survey. Retrieved from podium.com: http://learn.podium.com/rs/841-BRM-380/images/2017-SOOR-Infographic.jpg
