---
_id: '45616'
abstract:
- lang: eng
  text: 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.
author:
- first_name: Dirk
  full_name: van Straaten, Dirk
  id: '10311'
  last_name: van Straaten
- first_name: Vitalik
  full_name: Melnikov, Vitalik
  id: '58747'
  last_name: Melnikov
- first_name: Eyke
  full_name: Hüllermeier, Eyke
  id: '48129'
  last_name: Hüllermeier
- first_name: Behnud
  full_name: Mir Djawadi, Behnud
  id: '26032'
  last_name: Mir Djawadi
  orcid: 0000-0002-6271-5912
- first_name: René
  full_name: Fahr, René
  id: '111'
  last_name: Fahr
citation:
  ama: 'van Straaten D, Melnikov V, Hüllermeier E, Mir Djawadi B, Fahr R. <i>Accounting
    for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation
    Processes</i>. Vol 72.; 2021.'
  apa: 'van Straaten, D., Melnikov, V., Hüllermeier, E., Mir Djawadi, B., &#38; Fahr,
    R. (2021). <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary
    Approach on Aggregation Processes</i> (Vol. 72).'
  bibtex: '@book{van Straaten_Melnikov_Hüllermeier_Mir Djawadi_Fahr_2021, series={Working
    Papers Dissertations}, title={Accounting for Heuristics in Reputation Systems:
    An Interdisciplinary Approach on Aggregation Processes}, volume={72}, author={van
    Straaten, Dirk and Melnikov, Vitalik and Hüllermeier, Eyke and Mir Djawadi, Behnud
    and Fahr, René}, year={2021}, collection={Working Papers Dissertations} }'
  chicago: 'Straaten, Dirk van, Vitalik Melnikov, Eyke Hüllermeier, Behnud Mir Djawadi,
    and René Fahr. <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary
    Approach on Aggregation Processes</i>. Vol. 72. Working Papers Dissertations,
    2021.'
  ieee: 'D. van Straaten, V. Melnikov, E. Hüllermeier, B. Mir Djawadi, and R. Fahr,
    <i>Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach
    on Aggregation Processes</i>, vol. 72. 2021.'
  mla: 'van Straaten, Dirk, et al. <i>Accounting for Heuristics in Reputation Systems:
    An Interdisciplinary Approach on Aggregation Processes</i>. 2021.'
  short: 'D. van Straaten, V. Melnikov, E. Hüllermeier, B. Mir Djawadi, R. Fahr, Accounting
    for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation
    Processes, 2021.'
date_created: 2023-06-15T08:23:33Z
date_updated: 2023-07-05T07:27:17Z
intvolume: '        72'
language:
- iso: eng
project:
- _id: '8'
  grant_number: '160364472'
  name: 'SFB 901 - A4: SFB 901 - Empirische Analysen in Märkten für OTF Dienstleistungen
    (Subproject A4)'
- _id: '1'
  grant_number: '160364472'
  name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen
    in dynamischen Märkten '
- _id: '2'
  name: 'SFB 901 - A: SFB 901 - Project Area A'
series_title: Working Papers Dissertations
status: public
title: 'Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach
  on Aggregation Processes'
type: working_paper
user_id: '477'
volume: 72
year: '2021'
...