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ERIC Number: EJ1197033
Record Type: Journal
Publication Date: 2018-Dec
Pages: 8
Abstractor: As Provided
ISSN: ISSN-0273-4753
Is Unbiased? A Look at the Impact of Social Modelling on Student Online Reviews of Marketing Classes
Ackerman, David; Chung, Christina
Journal of Marketing Education, v40 n3 p188-195 Dec 2018
This article looks at how marketing student ratings of instructors and classes on online rating sites such as can be biased by prior student ratings of that class. Research has identified potential sources of bias of online student reviews administered by universities. Less has been done on the sources of bias inherent in a ratings site where those doing the rating can see prior ratings. To measure how student online ratings of a course can be influenced by existing online ratings, the study used five different prior ratings experiment conditions: mildly negative prior ratings, strongly negative prior ratings, mildly positive prior ratings, strongly positive prior ratings, and a control condition of no prior ratings. Results of this study suggest prior online ratings, both positive and negative, do affect subsequent online ratings and bias them. There are several implications. First, both negative and positive ratings can have an impact biasing subsequent ratings. Second, sometimes negative prior ratings must be strong in valence in order to bias subsequent ratings whereas even mildly positive ratings can have an impact. Last, this bias can potentially influence student course selection.
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Publication Type: Journal Articles; Reports - Research; Tests/Questionnaires
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A