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

Last registered on June 21, 2022

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Pre-Trial

Trial Information

General Information

Title

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

RCT ID

AEARCTR-0003656

Initial registration date

December 09, 2018

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

December 20, 2018, 10:14 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

June 21, 2022, 8:46 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

Germany

Region

Primary Investigator

Name

Dirk van Straaten

Affiliation

Paderborn University

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Behnud Djawadi

PI Affiliation

Paderborn University

Contact Investigator

Additional Trial Information

Status

On going

Start date

2016-12-19

End date

2022-09-30

Keywords

Other

Additional Keywords

customer reviews, aggregation, heuristics, binary bias, arithmetic mean

JEL code(s)

D12 D81 C91

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

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.

External Link(s)

Registration Citation

Citation

Djawadi, Behnud and Dirk van Straaten. 2022. "Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes." AEA RCT Registry. June 21. https://doi.org/10.1257/rct.3656-2.2

Former Citation

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Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2018-12-11

Intervention End Date

2018-12-12

Primary Outcomes

Primary Outcomes (end points)

Ranking of customer rating distributions

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We conduct a randomized controlled trial with a control group (baseline). In both groups, we run twelve periods containing three customer rating distributions each. For the purpose of incentivization we use real customer rating distributions in three periods whereby participants have the chance to win the associated products. These customer rating distributions have the unintended characteristic that at least one stochastically dominates at least one distribution out of this decision set. In the other periods subjects rank artificial customer rating distributions that have the feature of no (first-order) stochastic dominance.
Since it is crucial for our machine learning approach and its underlying assumptions,we plan to exclude decisions that contain stochastically dominated customer rating distributions for this part of analysis.

Experimental Design Details

Randomization Method

Randomization of the decision sets is done by a computer. E.g., decision set A is ranked by participant x in period k and by participant y in period f. In addition, the order the distributions are displayed (i.e., left, middle, right) is randomized by the computer.

Randomization Unit

individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

2 treatments: baseline (only customer rating distributions displayed) and information treatment (additionally, relative frequencies and the arithmetic mean displayed)

Sample size: planned number of observations

112 participants

Sample size (or number of clusters) by treatment arms

56 participants per treatment

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB