Evidence map›Paper›PMID 36160534›Full record

ArticleFrontiers in psychology2022

Visual Attention to Novel Products - Cross-Cultural Insights From Physiological Data.

Isabella Rinklin, Marco Hubert, Monika Koller, Peter Kenning

Abstract read
In one paragraph

Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Isabella RinklinDepartment of Corporate Management and Economics, Zeppelin University, Friedrichshafen, Germany.
Marco HubertDepartment of Management, Aarhus University, Aarhus, Denmark.
Monika KollerDepartment of Marketing, Vienna University of Economics and Business, Vienna, Austria.
Peter KenningFaculty of Business Administration and Economics, University of Düsseldorf, Düsseldorf, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study aims to investigate visual attention and perceived attractiveness to known versus unknown (novel) products above and beyond self-report applying physiological methods. A cross-cultural exploratory approach allows for comparing results gathered in the United States and China. We collected field data on physiological parameters accompanied by behavioral data. Mobile eye-tracking was employed to capture attention by measuring gaze parameters and electrodermal activity serves as indicator for arousal at an unconscious level. A traditional scale approach measuring perceived attractiveness of known versus novel products provide insights at a conscious level. US-American and Chinese consumers in our sample indeed process novel products differently. This can be observed at an unconscious and conscious level. Different gaze movements and arousal levels are observed at an unconscious level. Regarding behavioral data, the level of vertical orientation shapes the perceived attractiveness of novel products at a conscious level. The study showcases how physiological methods complement behavioral ones when investigating visual attention to products. It underlines varying conscious as well as unconscious visual attention and attractiveness ratings comparing known versus novel products, driven by cultural differences. Data from a field setting further enrich the implications derived for new product development and applied marketing.

Indexed as

attractiveness of novel productsconsumer neurosciencecross-cultural comparisonelectrodermal activitymobile eye-trackingvisual attention

Identifiers

PMID36160534
PMCPMC9496519

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.