Cited by 0Information Retrieval and Search BehaviorExpert finding and Q&A systemsEducational Strategies and Epistemologies

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index; 10 of the 52 sources its claims cite were checked for support.

From search results to generative answers: A mixed-methods study of credibility assessment and trust formation in AI Overviews

Open access
Casermeiro-Corpas, Yolanda, Rejón-Guardia, Francisco, Vallespín, María, Molinillo, Sebastián·2027·Information Processing & Management

Abstract

Generative search shifts information evaluation from comparing ranked documents to assessing synthesised answers. This study examines how users’ perceptions of answer quality are associated with trust and credibility in Google AI Overviews and explores whether visible interface cues may foster trust without systematic source verification. Study 1 analysed survey data from 510 Spanish users using a consistent partial least squares structural equation modelling approach. Study 2 combined eye tracking and interviews with 20 Spanish users. The model explained 75.7% of the variance in trust and 91.0% of the variance in credibility. Clarity and context ( β = .419, p < .001) and access to additional information and supporting sources ( β = .335, p = .002) showed the strongest observed associations with trust, whereas response immediacy was not significant ( β = .008, p = .874). Trust ( β = .834) and perceived usefulness ( β = .229) were positively associated with credibility (both p < .001), which, in turn, was associated with recommendation following ( β = .877) and intention to use ( β = .553; both p < .001). Eye tracking recorded no analysable fixations on the AI-error warning, while the interviews indicated that participants in this subsample did not routinely verify the displayed sources and often interpreted visible references as indicators of reliability. Taken together, Study 1 identifies associations between perceived answer-quality cues, trust, and credibility, whereas Study 2 suggests that visual prominence and visible references appeared to operate as authority cues for some participants and may encourage reliance without active source verification.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This mixed-methods study examines how users assess credibility and form trust in Google AI Overviews, combining a survey of 510 Spanish users with eye tracking and interviews with 20 Spanish users. It finds that clarity/context and access to supporting information are associated with trust, while visible references and default prominence can cue reliability without systematic source verification.

Key findings

  • In Study 1, the model explained 75.7% of variance in trust and 91.0% in credibility. Trust was most strongly associated with clarity and context (β = .419, p < .001), followed by adjustable depth (β = .335, p = .002) and social influence (β = .196, p < .001); response immediacy was not significant (β = .008, p = .874).
  • Trust was strongly associated with credibility (β = .834, p < .001), and perceived usefulness had a smaller positive association with credibility (β = .229, p < .001). Credibility was associated with recommendation following (β = .877, p < .001) and intention to use (β = .553, p < .001); recommendation following was also associated with intention to use (β = .305, p = .005).
  • Exploratory multigroup analysis found clarity/context was stronger for lower-innovativeness users (β = .719) than higher-innovativeness users (β = .219; Δβ = −.501, p = .001). Response immediacy and social influence were significant for higher-innovativeness users but not lower-innovativeness users (Δβ = .202, p = .038; Δβ = .208, p = .026).
  • Study 2 eye tracking showed attention concentrated on Main AI-generated content (M = 13.41 s fixation duration, M = 18.10 fixations, M = .65 s time to first fixation), with Additional information second (M = 7.96 s) and no analysable fixations on the AI-error warning. Interviews indicated 16 participants treated visible references as reliability cues, 17 did not notice the warning, and 9 described the default answer as reducing search effort.

Methods

  • Study 1 used a cross-sectional online survey collected in March 2026 from a Spanish consumer panel, retaining 510 adult respondents with prior search-engine experience. It estimated a PLSc-SEM in SmartPLS with 10,000 bootstrap subsamples, then ran an exploratory PLS-MGA by personal innovativeness (n = 462 after missing-data exclusion).
  • Study 2 collected eye-tracking and interview data in April 2026 from 20 Spanish university students (after excluding two recordings), using a Tobii Pro Nano at 60 Hz, six predefined AOIs, and a 30-second static simulated Spanish search query (“como moverse en roma”). Fixation metrics were compared with Friedman and Wilcoxon tests, and 60 verbal records were analysed inductively for themes.
  • The two datasets were analysed separately and integrated only at the interpretation stage.

Limitations

  • The paper states that Study 1 is cross-sectional and its paths are associations rather than causal effects; the Spanish panel had substantial prior GenAI exposure and may not represent less experienced populations; the median split for the exploratory MGA discards information and can create threshold-dependent differences; and the very high credibility R² = .910 may partly reflect conceptual proximity between credibility and its predictors.
  • The paper states that Study 2 involved a small, demographically homogeneous Generation Z university sample, a simulated interface, a single 30-second low-stakes travel-planning task, and no opportunity to scroll, open citations, expand the answer, or reformulate the query, so findings are explanatory and contextual rather than population-level or generalisable to high-stakes contexts.
  • The text also makes evident that the absence of analysable fixations on the AI-error warning does not rule out peripheral awareness or later recall, since some interviews reported noticing it; and because trust was not compared with objective answer accuracy, the findings are not evidence of trust calibration or miscalibration.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.ipm.2026.105211

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Image 1
CC-BY-NC-ND · From the PDF
Image 2
CC-BY-NC-ND · From the PDF
Fig. 2 Conceptual model and hypothesised
Fig. 2 Conceptual model and hypothesisedCC-BY-NC-ND · From the PDF
Fig. 3 Path coefficients by exploratory innovativeness profile. Note. * p < .05; *** p < .001. Bars with dashed outlines represent non-significant paths.
Fig. 3 Path coefficients by exploratory innovativeness profile. Note. * p < .05; *** p < .001. Bars with dashed outlines represent non-significant paths.CC-BY-NC-ND · From the PDF
Fig. 4 Areas of interest used in Study 2.
Fig. 4 Areas of interest used in Study 2.CC-BY-NC-ND · From the PDF
Image 6
CC-BY-NC-ND · From the PDF

Verification

Claims the paper makes

55 Own result · 18 Own method · 11 Not checked yet · 10 Own interpretation · 6 No full text of the sources · 3 Partly supported · 1 Supported · 1 Supported so far · 1 Not supported · 1 Sources not found · 1 Check pending · accuracy not yet measured

Verify all 13 references — free account
  • Probably not a substantial claim. 13
  • It may state two separate claims as one. 39
  • The quote only partly states this claim. 2
47 of 47
  • The paper relies on this without support.

    Google AI Overviews display the generative answer by default within the search results.

    “AIO differ because the generative answer is displayed by default within the search results.”
  • The paper relies on this without support.

    Generative search partially delegates content synthesis, source selection, and answer presentation to an AI system.

    “Generative search partially delegates content synthesis, source selection, and answer presentation to an AI system.”
  • The paper relies on this without support.

    Existing evidence does not establish a uniform direction for differences across the relationships included in the model.

    “However, existing evidence does not establish a uniform direction for these differences across the relationships included in the model.”
3 of 3

Self-check

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

Each contradiction between two passages listed here was confirmed by an independent check.

1 of 1

Citations

Cites

Show all 46 references

Research works printed but not linked: 1

  1. Ringle, 2024, Smartpls 4 [computer software], SmartPLS.

Cited by

Still being checked.

Metadata

Authors
Casermeiro-Corpas, Y., Rejón-Guardia, F., Vallespín, M., & Molinillo, S.
Affiliations
Universidad de Málaga (ES)
Venue
Information Processing & Management
Publisher
Elsevier BV
Published
2027-02
Volume
64
Issue
2
Pages
105211
ISSN
  • 0020-0271 (ISSN-L)
  • 0306-4573
OpenAlex
W7220864101
Cited by
0
References
46
FWCI
0.00
Type
journal article
Language
en
Open access
Locations
Information Processing & Management (publishedVersion, cc-by-nc-nd)
Funders
  • European Regional Development Fund (Research Group SEJ-567, PID2022-137648OB-C22)
  • Agencia Estatal de Investigación
  • University of Malaga
  • Ministerio de Ciencia e Innovación
  • España Ministerio de Ciencia e Innovación (PID2022-137648OB-C22)
Topics
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
Keywords
AI Overviews, generative search, trust in AI, credibility assessment, answer quality, source verification, visual prominence, eye tracking, Google, information evaluation, perceived usefulness, recommendation intention

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.

A language model drew the claims from the paper on 2026-11-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-11-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Cited sources are checked with a language model’s help (gpt-6-sol).

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