Personalized Online Recommendations and the Effect of Trust and Valence: An Abstract

Abstract

Online recommendations could be consumer-generated (Park et al. 2007) or personalized recommendations (West et al. 1999). Irrespective of the source, these recommendations help in improving choice satisfaction (Pu et al. 2012). In this study, we explore the moderating role of trust and review valence on the relationship among recommendation source, product price, offer attractiveness, and purchase intention. The primary objective of this paper was to examine the effect of product recommendation/review on offer attractiveness and purchase intentions. The present research extends previous work on online reviews, trust, and review valence. In two studies, we demonstrated how the effectiveness of product recommendations on consumer purchase intentions is dependent on trust, valence, and price level. In study 1, we found that automated recommendations are perceived as more attractive when the product was expensive and the trust in the website was low. On the other hand, this effect was reversed when the trust in the website was high. For lower priced products, lower risk is involved, and the consumers are attracted to the consumer recommendations even when the trust in the website is low. In study 2, we explored the effects of recommendation source and we also added review valence on offer attractiveness and consumer participation intentions and found the only effect was a three-way interaction with the price level. In particular, we found that for low-priced items and when the reviews were positive, consumer-generated recommendations were significantly more attractive than automated recommendations. The same effect was there at higher price levels and when the reviews were negative. When asked about their likelihood to purchase the item, consumers preferred the automated recommendation to the consumer recommendation when the prices were high and the review was positive. Results from this research shed new light on how consumers are influenced by online recommendations. Businesses may use these results to improve their online recommendation system to its full potential.