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Online reviews As a Tool For Communicating Reputation: Promise and Perils The present research also contributes to our understanding of broader issues related to the

문서에서 Incentives Can Reduce Bias in Online Reviews (페이지 26-46)

5. Discussion and Conclusion

5.4. Online reviews As a Tool For Communicating Reputation: Promise and Perils The present research also contributes to our understanding of broader issues related to the

advent of online reviews as a means of quickly propagating reputation in the marketplace. There are obvious advantages to online reviews. They are easily accessible and often free to use, and therefore have the potential to increase the efficiency of markets and allow consumers to make more informed and more optimal decisions.

However, online reviews also have less obvious disadvantages. First, consumers’ reviews of products and services lack objectivity, and often diverge from the opinions of experts (De Langhe, Fernbach, and Lichtenstein 2015). Second, the consumption of online reviews may not be systematic. Consumers may engage in selective or incomplete information search when evaluating reviews (Ariely, 2000; Urbany, Dickson, and Wilkie 1989). For example, existing research suggests that the characteristics of the choice set affect how people search for information. Larger choice sets (corresponding to websites with large amounts of consumer reviews) lead people to stop information search earlier in part to conserve cognitive resources (Diehl 2005; Payne, Bettman, and Johnson 1988; Iyengar, Wells, and Schwartz 2006). The order of the reviews people read can also affect when they stop searching for more information, as existing research finds that search strategies adopted in initial search environments tend to persist into different search environments (Broder and Schiffer 2006; Levav, Reinholtz, and Lin 2012).

The interpretation of online reviews may also be susceptible to information-processing biases observed in other domains (Kahneman and Tversky 1982). For example, consumers may not intuit the selection biases inherent in employer reviews, failing to appreciate the

non-representative polarity of the typical J-shaped review distribution. As another example, consumers may myopically focus on the “star” rating of a product or a service while failing to take into account the reference point upon which the rating is based. For example, a financial start-up company that has an average review score of 4.5 stars will likely differ from an established investment bank that has the same average review, because the reference point of employees working in these two companies differ in many ways. However, prospective employees may not fully account for these inherent differences when evaluating the two companies, and may instead myopically focus on their similar average ratings. Overall, while online reviews are an important tool for communicating reputation in the marketplace, their limitations can be consequential and warrant further study.

5.5. Conclusion

We assess the reliability of online employer reviews and the role that incentives can play in reducing bias in the distribution of employee opinions. Using data from a leading online

employer rating website Glassdoor, we have shown that voluntary reviews are more likely to be one star or five stars relative to incentivized reviews. On average, this difference in the

distribution leads to voluntary reviews being slightly more positive than incentivized reviews.

Using an experiment on Amazon’s Mechanical Turk, we show that voluntary employer reviews are biased relative to forced reviews. Forced reviews in our experimental setting provide an unbiased distribution of reviews that is not available in observational data from Glassdoor, and allow us to rigorously demonstrate bias in voluntary reviews. Our experiment allows us to show that certain monetary and pro-social incentives can increase the response rate and also reduce the bias in voluntary reviews.

Our results reinforce the conclusion from the existing word-of-mouth literature that users should know that the distribution of voluntary online reviews can be biased and should be taken with a grain of salt. At the same time, we also demonstrate that bias in reviews can be reduced by using adequate incentives. Our results suggest that websites and government entities alike should experiment with the use of incentives – monetary and pro-social – to increase survey response rates and thereby reduce bias. Such experimentation has the promise to both improve the quality of information at consumers’ disposal and allow companies and governments to optimize the informative signal contained in their surveys.


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Mturk Glassdoor

Variable Observations Mean SE Observations Mean SE

Age 639 35.462 10.887 188,623 34.314 10.550

Female 639 0.518 0.500 188,623 0.419 0.493

More than 1000 employees 639 0.421 0.494 188,623 0.703 0.457

Education (years) 639 15.283 2.006 188,623 15.505 1.343

Tenure (years) 639 3.563 4.344 188,623 3.543 4.790

Table 1: Summary statistics: Mturk vs. Glassdoor datasets

Rating Rating Is 1 star Is 1 star Is 5 stars Is 5 stars

(1) (2) (3) (4) (5) (6)

Voluntary 0.0240*** 0.0345*** 0.0190*** 0.0143*** 0.0467*** 0.0432***

(0.00800) (0.00798) (0.00169) (0.00169) (0.00301) (0.00302)

Age -0.00881*** 0.00225*** -0.000635***

Observations 188,623 188,623 188,623 188,623 188,623 188,623

R-squared 0.000 0.022 0.001 0.011 0.001 0.035

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Table 2: Glassdoor selection bias: more polarized ratings


Table 3: Impact of selection and incentives on average ratings in MTurk sample (relative to no incentive, forced review)


Table 4: Impact of selection and incentives on average ratings for incentives that did not increase response rates in the MTurk sample (relative to no incentive, forced review)

Rating Rating

Negative prosocial -0.325 -0.319

(0.198) (0.199) Nonspecific prosocial -0.332* -0.345*

(0.190) (0.194)

Positive prosocial -0.332* -0.331*

(0.181) (0.182) Choice*

Control (no incentive) -0.574** -0.574**

(0.225) (0.227)

Monetary high -0.294 -0.297

(0.212) (0.215)

Monetary low -0.311 -0.323

(0.241) (0.243)

Negative prosocial 0.278 0.273

(0.241) (0.236)

Nonspecific prosocial 0.382* 0.402*

(0.212) (0.213)

Positive prosocial 0.043 0.029

(0.252) (0.253)

*** p<0.01, ** p<0.05, * p<0.1

Table 5: Framing effects: do incentives affect forced average ratings in MTurk sample?

Figure 1: Glassdoor GTG vs. voluntary reviews

Figure 2: Glassdoor: changes in rankings of frequent industries due to bias

Figure 3: MTurk experiment: efficacy of different incentives in increasing response rates












Control Positive Prosocial Negative Prosocial Nonspecific Prosocial

Monetary Low Monetary High

Proportion Choosing to Provide Review

Experimental Condition

Figure 4: Bias in reviews in the absence of incentives in the MTurk experiment

Figure 5: No bias in reviews with high monetary incentive (75% payment increase) in the MTurk experiment

Figure 6: No bias in reviews with nonspecific prosocial incentives in the MTurk experiment

Figure 7: Glassdoor GTG vs. Mturk choice, high monetary: similar distributions

문서에서 Incentives Can Reduce Bias in Online Reviews (페이지 26-46)

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