The value of online reviews has been well documented by academics and practitioners. However, to maximise the benefits of consumer reviews, online sellers must avoid the negative consequences associated with customer feedback, such as reputation loss, or product returns after purchase. In developing a better understanding of the relationships between online reviews and their potential for negative impacts, this research aims to explore product returns. Through a quantitative model, this research demonstrates why online reviews can result in product return behaviours.
The hypotheses were tested via two studies. In Study 1, the authors examine the direct effects of review valence and review volume on product returns by analysing secondary data on 4,995 stores on China's Taobao.com. Study 2 further extends and validates the findings of Study 1 with a survey sample of 795 participants across several online shopping platforms. This analysis examines the mechanics and conditions that influence the relationships between online reviews and product returns through partial least squares-structural equation modelling (PLS-SEM).
The results show that both review valence (i.e. average star ratings) and the number of reviews can increase the probability of product returns due to the high expectations that result from positive online reviews. Further, the effect of review valence on product returns is stronger for first-time purchasers at a store. In terms of mitigation, the analysis shows that bilateral communications between sellers and buyers can temper the unrealistic expectations set by positive reviews, leading to fewer product returns.
This research adds to the literature on online reviews by exploring the negative consequences of online reviews and the role they play in online purchasing decisions. The findings also provide direct evidence as to why online reviews can result in more product returns, adding clarity to extant research which contains conflicting conclusions as to how online reviews affect product return behaviours.
