There exists evidence that online reviews in general and primary (or initial) reviews, in particular, are manipulated and biased by the sellers to increase their reputation. And, primary reviews may provide little information on consumers’ experience of the products since most consumers provide them a few days after the purchase. To overcome the present weakness of primary reviews, such leading platforms as Taobao, the largest C2C marketplace globally, introduced a new type of review, i.e., supplementary (or additional) reviews, which consumers usually do not provide until the products are actually experienced. The present research explores how the interplay of primary reviews and supplementary ones influences consumer decision making, and what sellers should do to manage the ensued inconsistency and consistency by proposing an ambivalence–confidence framework based on the heuristic-systematic model. Moreover, it shows that truthfulness of online reviews and seller responses act as additional heuristics, which bias the systematic processing to mitigate the detrimental effects of inconsistent reviews. Accordingly, this research recommends that e-commerce platforms and sellers should facilitate unbiased reputation systems to encourage truthful reviews to mitigate the adverse effects of inconsistent reviews and strengthen the intention to buy in case of consistent reviews.

有证据表明，一般来说，一般评论和主要（或初始）评论中的在线评论受到卖方的操纵和偏见，以提高其声誉。并且，由于大多数消费者在购买后的几天内提供产品，因此初级评论可能很少提供有关消费者对产品体验的信息。为了克服初级评论的当前弱点，领先的平台（例如，全球最大的C2C市场淘宝网）引入了一种新型的评论，即补充（或附加）评论，消费者通常在实际体验产品后才提供该评论。本研究探讨了主要评论和补充评论之间的相互作用如何影响消费者的决策，以及卖方应采取什么措施，通过提出基于启发式系统模型的矛盾度-置信度框架，来管理随之而来的不一致和一致性。此外，它表明在线评论和卖家响应的真实性还可以作为启发式方法，这会偏向系统化处理，以减轻不一致评论的不利影响。因此，本研究建议电子商务平台和卖家应促进公正的信誉系统，以鼓励真实的评论以减轻不一致评论的不利影响，并在一致评论的情况下加强购买意愿。这会偏重系统处理，以减轻不一致的评论的不利影响。因此，本研究建议电子商务平台和卖家应促进公正的信誉系统，以鼓励真实的评论以减轻不一致评论的不利影响，并在一致评论的情况下加强购买意愿。这会偏重系统处理，以减轻不一致的评论的不利影响。因此，本研究建议电子商务平台和卖家应促进公正的信誉系统，以鼓励真实的评论以减轻不一致评论的不利影响，并在一致评论的情况下加强购买意愿。