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Assessing advertisement quality on C2C social commerce platforms: an information quality approach using text mining

Deepak Trehan (Marketing, Indian Institute of Management Kashipur , Kashipur, India)
Rajat Sharma (Marketing, Indian Institute of Management , Ahmedabad, India)

Online Information Review

ISSN: 1468-4527

Article publication date: 27 October 2020

Issue publication date: 27 January 2021




The purpose of this paper is to test relevance of the information quality (IQ) framework in understanding quality of advertisements (ads) posted by ordinary consumers.


The main objective of this study is to assess quality ads posted on customer-to-customer (C2C) social commerce platforms from an IQ framework. The authors deployed innovative text mining techniques to generate features from the IQ framework and then used a machine learning (ML) algorithm to classify ads into three categories ‐ high quality, medium quality and low quality.


The results show that not all dimensions of IQ framework are important to assess quality of ads posted on the platforms. Potential buyers on these platforms look for appropriate amount of information, which is objective, concise and complete, to make a potential purchase decision.

Research limitations/implications

As the research focuses on specific product categories, it lacks generalisability. Therefore, it needs to be tested for other product categories.

Practical implications

The paper includes recommendation for C2C marketplaces on how to increase quality of ads posted by consumers on the platform.


This study has focused on the user-generated content posted by ordinary consumers on the C2C commerce platform to sell used goods. Though C2C model has been developed on ads posted on C2C platforms, it can be established for brands as it provides them with an insight into latent dimensions that a consumer shall look for in an ad on social commerce platforms.



Trehan, D. and Sharma, R. (2021), "Assessing advertisement quality on C2C social commerce platforms: an information quality approach using text mining", Online Information Review, Vol. 45 No. 1, pp. 46-64.



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