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Complicating or simplifying? Investigating the mixed impacts of online product information on consumers’ purchase decisions

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Prior literature indicates conflicting effects of online product information, which may complicate or simplify consumer purchase decisions. Therefore, the purpose of this paper is to investigate how different online product information (i.e. the choice set size and the popularity information and its presentation) affect consumers’ decision making and the related market outcomes.


This research relies on information-processing theories and social learning theory. By stepwise conducting two 2×2 within-subject factorial design experiments, this research examines the effects of the choice set size, product popularity information and product presentation on consumers’ decision making and the aggregated market outcomes.


The results show that product popularity information led consumers to either simplify or complicate their decision strategy, depending on the size of the choice sets. Additionally, presenting products by their popularity in descending order resulted in consumers making decisions with a larger decision bias. The results also show that the presence of product popularity was more likely to forge a “superstar” structure in a large market.

Practical implications

The research suggests that e-retailers and e-marketplace operators should carefully utilize product popularity information. Multiple mechanisms that shape different shopping environments with different orders are necessary to create a long-tailed market structure.


This study found the mixed effects of product popularity information when it is presented in different environments (i.e. the large/small choice set and the sorted/randomized product presentation). The overuse of popularity information may induce consumers’ decision bias.
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Keywords: Cognitive effort; Decision bias; Information presentation; Market outcomes; Social learning

Document Type: Research Article

Affiliations: 1: School of Information, Renmin University of China, Beijing, China 2: Department of Information Systems, College of Business, City University of Hong Kong, Kowloon, Hong Kong 3: School of Economics and Management, Beihang University, Beijing, China 4: Department of Applied Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA

Publication date: February 23, 2020

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