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Aggregation processes in customer rating systems
Aggregation processes in reputation systems
Derivation of aggregation functions
Focus on negative ratings (NEG). Similarly, there is growing evidence that consumers[...]
Aggregation processes in reputation systems
Aggregation metrics background
Derivation of aggregation functions
Arithmetic mean (AM). We commence our analysis with the arithmetic mean where the valence score is calculated by weighting the rating categories in proportion to their scale values. The intuitive interpretation, ease of computation, and documented correla
Focus on the highest category (FIV). As an alternative to the arithmetic mean, our first approach posits that customers calculate a product’s valence by prioritizing five-star reviews. This assumption is grounded in the prevalence of J-shaped rating distr
Focus on the lowest category (ONE). Likewise customers may put emphasis on the lowest category to capture the product’s valence. Empirical evidence from [21] demonstrates a shift from unidimensional to multidimensional reputation systems, revealing that t
Binary perception of ratings (BIN). [23] provide evidence for a cognitive simplification pattern they term the binary bias in consumer evaluation of rating distributions. Their findings indicate that individuals tend to categorize ratings in a dichotomous
Focus on positive ratings (POS). Empirical evidence suggests that 5-star ratings may not always serve as a reliable indicator of true product quality, as they can be subject to various cognitive biases and strategic manipulation (e.g., [24,25]). Consequen
Focus on negative ratings (NEG). Similarly, there is growing evidence that consumers who place heightened emphasis on negative reviews may not rely exclusively on 1-star ratings, particularly due to skepticism regarding the authenticity of online feedback
Median (MED). As a final aggregation function, we consider the median, which is defined as the central value in an ordered ranking of all observed ratings. Specifically, the ratings are sorted in ascending order, and the median corresponds to the category
Experimental design
Data and statistical model
Plackett-Luce model
Product preferences
Parameter estimation
Results
Descriptive statistics
Aggregation behavior derived from statistical model
Heterogeneity in aggregating product information distributions
Impact of numerical information
Conclusion
Supporting information
References
Aufsatz in einer Zeitschrift
Aggregation processes in customer rating systems : insights from an economic decision experiment / Dirk van Straaten, Behnud Mir Djawadi , Vitalik Melnikov, Eyke Hüllermeier, René Fahr
Entstehung
Paderborn
2026
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