 "Online user reviews can mislead you to choose wrong products, says study")
Do you often check user ratings before buying products online? You may be misled into making the wrong choice, a study suggests.
When buying products online, people often rely on the ratings and reviews of others to choose.
However, scientists suggest that people tend to use this information in ways that can work to their disadvantage.
The findings, published in the journal Psychological Science, indicate that people tend to favor a product that has more reviews, even when it has the same low rating as an alternative product.
"It's extremely common for websites and apps to display the average score of a product along with the number of reviews," said Derek Powell of Stanford University.
"We found that people were biased toward choosing to purchase more popular products and that this sometimes led them to make very poor decisions," Powell said.
As opportunities to buy products and services online multiply, we have greater access than ever before to huge amounts of first-hand information about users' experiences.
"We wanted to examine how people use this wealth of information when they make decisions and how they weigh information about other people's decisions with information about the outcomes of those decisions," said Powell.
Looking at actual products available on Amazon.Com, researchers found no relationship between the number of reviews a product had and its average rating.
In other words, real-world data show that a large number of reviews is not a reliable indicator of a product's quality.
Researchers wanted to see how people would actually use review and rating information when choosing a product.
In one online experiment, 132 adult participants looked at a series of phone cases, presented in pairs.
The participants saw an average user rating and total number of reviews for each phone case and indicated which case in each pair they would buy.
Across various combinations of average rating and number of reviews, participants routinely chose the option with more reviews.
This bias was so strong that they often favored the more-reviewed phone case even when both of the options had low ratings, effectively choosing the product that was, in statistical terms, more likely to be low quality.
A second online experiment that followed the same design and procedure produced similar results.
"By examining a large dataset of reviews from Amazon.Com, we were able to build a statistical model of how people should choose products," said Powell.
"We found that, faced with a choice between two low- scoring products, one with many reviews and one with few, the statistics say we should actually go for the product with few reviews, since there's more of a chance it's not really so bad," he said.
"But participants in our studies did just the opposite: They went for the more popular product, despite the fact that they should've been even more certain it was of low quality," he added.
The researchers found that this pattern of results fit closely with a statistical model based on social inference.
That is, people seem to use the number of reviews as shorthand for a product's popularity, independent of the product's average rating.
When buying products online, people often rely on the ratings and reviews of others to choose.
However, scientists suggest that people tend to use this information in ways that can work to their disadvantage.
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"It's extremely common for websites and apps to display the average score of a product along with the number of reviews," said Derek Powell of Stanford University.
"We found that people were biased toward choosing to purchase more popular products and that this sometimes led them to make very poor decisions," Powell said.
As opportunities to buy products and services online multiply, we have greater access than ever before to huge amounts of first-hand information about users' experiences.
"We wanted to examine how people use this wealth of information when they make decisions and how they weigh information about other people's decisions with information about the outcomes of those decisions," said Powell.
Looking at actual products available on Amazon.Com, researchers found no relationship between the number of reviews a product had and its average rating.
In other words, real-world data show that a large number of reviews is not a reliable indicator of a product's quality.
Researchers wanted to see how people would actually use review and rating information when choosing a product.
In one online experiment, 132 adult participants looked at a series of phone cases, presented in pairs.
The participants saw an average user rating and total number of reviews for each phone case and indicated which case in each pair they would buy.
Across various combinations of average rating and number of reviews, participants routinely chose the option with more reviews.
This bias was so strong that they often favored the more-reviewed phone case even when both of the options had low ratings, effectively choosing the product that was, in statistical terms, more likely to be low quality.
A second online experiment that followed the same design and procedure produced similar results.
"By examining a large dataset of reviews from Amazon.Com, we were able to build a statistical model of how people should choose products," said Powell.
"We found that, faced with a choice between two low- scoring products, one with many reviews and one with few, the statistics say we should actually go for the product with few reviews, since there's more of a chance it's not really so bad," he said.
"But participants in our studies did just the opposite: They went for the more popular product, despite the fact that they should've been even more certain it was of low quality," he added.
The researchers found that this pattern of results fit closely with a statistical model based on social inference.
That is, people seem to use the number of reviews as shorthand for a product's popularity, independent of the product's average rating.