Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,441

The DOI matches its OpenAlex record; 48 of the 48 references it cites were found in a registry or an index.

Task-Dependent Algorithm Aversion

Noah Castelo, Maarten W. Bos, Maarten W. Bos, Donald R. Lehmann, Donald R. Lehmann·2019·Springer eBooks
DOI 10.1007/978-3-031-88067-4_21
Open this paper in CoChat — free account

Abstract

Research suggests that consumers are averse to relying on algorithms to perform tasks that are typically done by humans, despite the fact that algorithms often perform better. The authors explore when and why this is true in a wide variety of domains. They find that algorithms are trusted and relied on less for tasks that seem subjective (vs. objective) in nature. However, they show that perceived task objectivity is malleable and that increasing a task’s perceived objectivity increases trust in and use of algorithms for that task. Consumers mistakenly believe that algorithms lack the abilities required to perform subjective tasks. Increasing algorithms’ perceived affective human-likeness is therefore effective at increasing the use of algorithms for subjective tasks. These findings are supported by the results of four online lab studies with over 1,400 participants and two online field studies with over 56,000 participants. The results provide insights into when and why consumers are likely to use algorithms and how marketers can increase their use when they outperform humans.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

OpenAlex · 10.1007/978-3-031-88067-4_21

Cites

48 of 48 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 48 references

Cited by

See all 1,441

Metadata

Authors
Castelo, N., Bos, M. W., Bos, M. W., Lehmann, D. R., & Lehmann, D. R.
Venue
Springer eBooks
Publisher
Springer Nature
Published
2019-07-15
Volume
56
Issue
5
Pages
507-538
DOI
10.1007/978-3-031-88067-4_21
OpenAlex
W2961720344
Cited by
1,441
References
48
FWCI
84.03
Type
book chapter
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Task-Dependent Algorithm Aversion. https://app.cochat.ai/papers/10.1007/978-3-031-88067-4_21
The paper
Castelo, N., Bos, M. W., Bos, M. W., Lehmann, D. R., & Lehmann, D. R. (2019). Task-Dependent Algorithm Aversion. Springer eBooks. https://doi.org/10.1007/978-3-031-88067-4_21

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.