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An analysis of process-tracing research on consumer decision-making
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“Interestingly, many authors, who deal with think-aloud, also recommend combining them with eye-tracking [47,54,64,65,67,71].”
From Impact of Think-Aloud on Eye-Tracking: A Comparison of Concurrent and Retrospective Think-Aloud for Research on Decision-Making in the Game Environment (2020) · cites this paper as [47]
In the citing paper
“Therefore, the combination of eye-tracking with other methods is necessary [21,47,50].”
From Impact of Think-Aloud on Eye-Tracking: A Comparison of Concurrent and Retrospective Think-Aloud for Research on Decision-Making in the Game Environment (2020) · cites this paper as [47]
In the citing paper
“The involvement of eye-tracking can help solve the shortcomings of think-aloud because it allows one to confirm and extend the gathered data [47] or to complete omitted and forgotten points [54].”
From Impact of Think-Aloud on Eye-Tracking: A Comparison of Concurrent and Retrospective Think-Aloud for Research on Decision-Making in the Game Environment (2020) · cites this paper as [47]
In the citing paper
“Scientists, who have described deficiencies of verbal protocols, recommend their combination with eye-tracking, which helps to complete missing data and insights [47,54,64,65].”
From Impact of Think-Aloud on Eye-Tracking: A Comparison of Concurrent and Retrospective Think-Aloud for Research on Decision-Making in the Game Environment (2020) · cites this paper as [47]
In the citing paper
“Many authors recommend using a combination of verbal protocols and eye-tracking to investigate cognitive processes [47,54,64,65,67,71]”
From Impact of Think-Aloud on Eye-Tracking: A Comparison of Concurrent and Retrospective Think-Aloud for Research on Decision-Making in the Game Environment (2020) · cites this paper as [47]
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Cited by
Wörfel, P., Frentz, F., & Tautu, C. (2022) · European Journal of Marketing · doi:10.1108/ejm-07-2020-0510 · Cited by 84
Prokop, M., Pilař, L., & Tichá, I. (2020) · Sensors · doi:10.3390/s20102750 · Cited by 48
Metadata
- Authors
- Zuschke, N.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 111
- Pages
- 305-320
- ISSN
- 0148-2963
- Cited by
- 68
- References
- 22
- Language
- en
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
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 ›
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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.