Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes

Dirk van Straaten (), Vitalik Melnikov (), Eyke Hüllermeier (), Behnud Mir Djawadi and René Fahr ()
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Dirk van Straaten: Paderborn University
Vitalik Melnikov: Paderborn University
Eyke Hüllermeier: Ludwig-Maximilians-University Munich
René Fahr: Paderborn University

No 72, Working Papers Dissertations from Paderborn University, Faculty of Business Administration and Economics

Abstract: Aggregation metrics in reputation systems are important for overcoming information overload. When using these metrics, technical aggregation functions such as the arithmetic mean are implemented to measure the valence of product ratings. However, it is unclear whether the implemented aggregation functions match the inherent aggregation patterns of customers. In our experiment, we elicit customers' aggregation heuristics and contrast these with reference functions. Our findings indicate that, overall, the arithmetic mean performs best in comparison with other aggregation functions. However, our analysis on an individual level reveals heterogeneous aggregation patterns. Major clusters exhibit a binary bias (i.e., an over-weighting of moderate ratings and under-weighting of extreme ratings) in combination with the arithmetic mean. Minor clusters focus on 1-star ratings or negative (i.e., 1-star and 2-star) ratings. Thereby, inherent aggregation patterns are neither affected by variation of provided information nor by individual characteristics such as experience, risk attitudes, or demographics.

Keywords: customer reviews; aggregation; heuristics; binary bias; arithmetic mean (search for similar items in EconPapers)
JEL-codes: C91 D12 D81 (search for similar items in EconPapers)
Pages: 42
Date: 2021-04
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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