Incentives Can Reduce Bias in Online Reviews

Marinescu, Ioana; Klein, Nadav; Chamberlain, Andrew; Smart, Morgan

doi:10.3386/w24372

Cited by 28 publications

(15 citation statements)

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“…Can we improve institutional designs to better elicit, aggregate, and disseminate information about employers? Platform design affects workers' willingness to voluntarily contribute their private information to the public pool (Marinescu et al 2018). A policy example of this kind of logic in action is that, in 2009, the U.S. Occupational Safety and Health Administration began systematically issuing press releases to notify the public about large violations of workplace safety laws.…”

Section: Lessons For Reputation Systems In Off-linementioning

confidence: 99%

Can Reputation Discipline the Gig Economy? Experimental Evidence from an Online Labor Market

2020

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Please scroll down for article-it is on subsequent pages With 12,500 members from nearly 90 countries, INFORMS is the largest international association of operations research (O.R.) and analytics professionals and students. INFORMS provides unique networking and learning opportunities for individual professionals, and organizations of all types and sizes, to better understand and use O.R. and analytics tools and methods to transform strategic visions and achieve better outcomes. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org

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Section: Lessons For Reputation Systems In Off-linementioning

confidence: 99%

Can Reputation Discipline the Gig Economy? Experimental Evidence from an Online Labor Market

2020

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“…11 Though we expect that our perception and tax avoidance news measures offer several benefits, we acknowledge that both measures suffer from potential selection bias. However, Marinescu et al (2018) and Liu et al (2018) suggest that Glassdoor.com ratings suffer from minimal selection (self-reporting) bias and that reviewers receive salaries representative of the salary distribution of employees in major metropolitan areas. Our tax avoidance news variable may suffer from selection bias if the firms most likely to suffer reputation effects choose not to identify news sources that are not salient to employees because (1) local news coverage may only be seen by local employees and (2) tax news may cover firms' subsidiaries and employees do not identify with the subsidiary.…”

Section: Data Variables and Samplementioning

confidence: 99%

The Effects of Tax Avoidance News on Employee Perceptions of Managers and Firms: Evidence from Glassdoor.com Ratings

Lee

Shevlin

et al. 2020

SSRN Journal

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We examine whether employee perceptions of managers and firms fall following tax avoidance news. Using S&P 500 firms and generalized difference-indifferences specifications, we find that tax avoidance news negatively affects employee perceptions of managers and firms. In cross-sectional tests, we find that (1) firms and managers in consumerfacing industries suffer larger employee-related perception changes from tax avoidance news compared to other firms, and (2) well-performing firms and their managers face smaller perception changes than other firms and managers. Overall, our results are consistent with tax avoidance news negatively affecting employee perceptions of managers and firms.

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“…Numerous works have studied online employer reviews and most works focused on the reviewing platform glassdoor 1 . For example, Marinescu et al [41] described a selection bias in online reviews, meaning that people with extreme opinions are more motivated to share their experiences as compared to people with moderate opinions. Chandra [6] depicted differences in work-life balance between eastern and western countries using reviews written on glassdoor.…”

Section: Related Workmentioning

confidence: 99%

Employee Satisfaction in Online Reviews

Koncar

Helić

2020

Lecture Notes in Computer Science

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Employee satisfaction impacts the efficiency of businesses as well as the lives of employees spending substantial amounts of their time at work. As such, employee satisfaction attracts a lot of attention from researchers. In particular, a lot of effort has been previously devoted to the question of how to positively influence employee satisfaction, for example, through granting benefits. In this paper, we start by empirically exploring a novel dataset comprising two million online employer reviews. Notably, we focus on the analysis of the influencing factors for employee satisfaction. In addition, we leverage our empirical insights to predict employee satisfaction and to assess the predictive strengths of individual factors. We train multiple prediction models and achieve accurate prediction performance (ROC AUC of best model $$=0.89$$ = 0.89 ). We find that the number of benefits received and employment status of reviewers are most predictive, while employee position has less predictive strengths for employee satisfaction. Our work complements existing studies and sheds light on the influencing factors for employee satisfaction expressed in online employer reviews. Employers may use these insights, for example, to correct for biases when assessing their reviews.

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Incentives Can Reduce Bias in Online Reviews

Cited by 28 publications

References 0 publications

Can Reputation Discipline the Gig Economy? Experimental Evidence from an Online Labor Market

Can Reputation Discipline the Gig Economy? Experimental Evidence from an Online Labor Market

The Effects of Tax Avoidance News on Employee Perceptions of Managers and Firms: Evidence from Glassdoor.com Ratings

Employee Satisfaction in Online Reviews

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