Intuition First or Reflection Before Judgment? The Impact of Evaluation Sequence on Consumer Ratings

TL;DR

This study examines how the order of rating and review prompts influences consumer ratings, revealing that sequence effects vary with service quality and product type, impacting rating polarization and authenticity.

Contribution

It uncovers the polarization effect of evaluation sequence on ratings, moderated by service quality and product attributes, through experiments and large-scale data analysis.

Findings

Rating-First increases ratings in high-quality contexts

Rating-First decreases ratings in low-quality contexts

Platforms show different rating distributions based on interface design

Abstract

As online reviews increasingly drive consumer decisions, the impact of review interface design on rating authenticity remains under-explored. This research investigates how evaluation sequence ("Rating-First" vs. "Review-First") influences consumer ratings through three experiments and a large-scale secondary data analysis. The results reveal a significant polarization effect: in high-quality service contexts, the "Rating-First" sequence (vs. "Review-First") increases overall ratings, whereas in low-quality contexts, it leads to significantly lower ratings. This mechanism is driven by a serial mediation path of affective heuristics and cognitive effort. Furthermore, product attributes moderate this effect, with hedonic products amplifying the rating extremity compared to utilitarian ones. Secondary data from Yelp and Letterboxd confirm these findings, showing that the "Rating-First" platform (Yelp) exhibits a polarized bimodal distribution, while the "Review-First" platform (Letterboxd) shows more concentrated ratings. In conclusion, this research reveals how evaluation sequence shapes consumer ratings through affective and cognitive paths from an information-processing perspective. These findings extend the theoretical understanding of the online review formation process and offer practical insights for platforms to optimize interface design and enhance rating authenticity and credibility.